Abstract

     We analyze the likelihood of financial statement manipulation among 19 listed manufacturing and trading firms on the Ghana Stock Exchange for the period 2008 to 2017. We use the Beneish model to group the firms into those likely to engage in financial statement manipulation and those not likely to be involved in financial statement manipulation. Generally, the results show that majority of the firms are likely to be involved in financial statement manipulation. Also, we find that profitability, liquidity, financial leverage, change of audit firm, and the overall economic condition (Z-score) are firm-level factors that predict the likelihood of financial statement manipulation among listed manufacturing and trading firms in Ghana. Given the high number of manufacturing and trading firms likely to engage in financial statement manipulation, there is the need for the managers of the Ghana Stock Exchange to subject future financial reports of these firms to rigorous scrutiny to safeguard the interest of their stakeholders.

Introduction

     Three objectives drive this study. First, the study seeks to detect the likelihood of financial statement manipulation1 among listed firms in Ghana using the Beneish (1999) model. Second, it examines whether financial ratios characteristics differ among firms that are likely to engage in financial statement manipulation and those not likely to manipulate their financial statements. Third, it determines the financial ratios2 that predict the likelihood of financial statement fraud.

     The fundamental purpose of corporate financial reporting is to provide the actual state of affairs of an entity to its stakeholders. The rationale is that users of financial reports found their economic decisions on information contained in such reports (Kanapickienė & Grundienė, 2015). It is for this reason that financial statement fraud is considered a serious legal and ethical issue in accounting practice. In this study, financial statement fraud is defined as any act of omission or misstatement deliberately done or committed by a firm in its financial reporting that distorts the entity’s real state of affairs. This definition, to a considerable extent, dovetails into that of Apostolou, Hassell, and Webber (2000), who define corporate financial fraud as the “intentional misrepresentation of amounts or disclosures in the financial statements” (p.181).

Conclusion

     The research seeks to detect financial statement manipulation among 19 listed manufacturing and trading firms in Ghana using the Beneish model and financial ratios. The study covers 2008 to 2017 fiscal years. We use a probit regression technique to predict the probability of a listed manufacturing and trading firm engaging in corporate financial fraud. The results demonstrate that the majority of listed manufacturing and trading firms are likely to engage in financial statement manipulation. The study, therefore, concludes that financial statement manipulation may be prevalent among listed manufacturing and trading firms in Ghana. Again, sales to fixed assets ratio (SAL/FA), total debt to total assets ratio (TD/TA), earnings before interest and tax to sales ratio (EBIT/ SAL), gross profit to sales ratio (GP/SAL), cost of sales to sales ratio (COS/SAL), working capital to total assets (WC/TA), current assets to current liabilities (CA/CL) and Z-score are the financial ratios that differ significantly between firms likely to engage in corporate financial statement fraud and those not likely to do so. A probit regression analysis has shown that profitability, overall financial condition, liquidity ratios as well as a change of their external auditors predict the likelihood of listed manufacturing and trading firms getting involved in financial statement manipulation.

1- Introduction

2- Literature review

3- Research questions and hypotheses

4- Data and methodology

5- Results

6- Conclusion

References

Abstract

Stock exchange mergers can lead to increased efficiency; however, increasing levels of concentration can potentially lead to the exercise of market power. We investigate the market power repercussions of stock exchange mergers and find that the industry’s concentration levels have not significantly increased and the concentration levels do not influence exchanges’ profitability in the post-merger period. The profitability of the merging exchanges in the post-merger period is largely influenced by efficiencies in revenue generation and cost management. The absence of evidence that stock exchange mergers lead to the exercise of market power suggests that there does not appear to be an immediate need for regulatory agencies to be overly concerned about mergers among stock exchanges leading to the exploitation of market power to the detriment of consumer welfare.

Introduction

Financial markets have witnessed a wave of mergers among leading stock exchanges involving the NYSE, Euronext, Nasdaq, London Stock Exchange (LSE), and Deutsche Boerse, among others. Stock exchange mergers and acquisitions continue to gain prominence largely because of the expected gains from these combinations. Although there are several sources of gains that can emanate from mergers and acquisitions,1 efficiency gains and market power gains are two of the most important sources of gains widely discussed in the extant literature (e.g., White, 1987; Bhattacharyya and Nain, 2011; Devos et al., 2016). Whereas efficiency gains are positively perceived because of the possibility that improvements in efficiency can lead to lower prices, gains resulting from market power are regarded in a negative light because they lessen competition and can lead to restricted output, higher prices, and consequently allocative inefficiency (De Loecker and Eeckhout, 2018). The conventional expectation is that mergers and acquisitions, especially horizontal mergers among a subset of firms in the same market, may reduce competition and increase market concentration (Levin, 1990; Bhattacharyya and Nain, 2011; Bernile and Lyandres, 2019).

In this work, the impact of oil price shocks on the stock exchanges of three countries in the Caspian Basin − Iran, Kazakhstan and Russia − was examined through a structural vector autoregression (SVAR) model. For the research, monthly data from the stock exchanges, the oil price, inflation, industrial production and exchange rates were collected between March 2005 and June 2018. According to the results of variance decomposition, in these three countries, the impact of negative oil price shocks on the stock exchanges was stronger than that of positive shocks, and constituted the largest source of changes in the three stock exchanges. In addition, according to the results of impulse response functions, the response of the stock exchanges in the three countries to negative oil shocks was highly significant. Consequently, these countries should avoid macroeconomic imbalances and falls in their stock exchanges due to the negative impact of the oil price, and should instead focus on industrial production that will contribute to exports. In this way, they can avoid the negative impact of oil price shocks on their stock exchanges.

Introduction

In this work, the main aim was to examine the asymmetric impact of Brent oil price shocks on the stock exchanges of three Caspian Basin countries through structural vector autoregression (SVAR). It is known that Caspian Basin countries are not globally strong in medium- and high-tech industrial production,1 and that they are highly dependent on oil production for export. Moreover, their own economies are susceptible to positive or negative impacts from oil prices. In this research, it is assumed that stock exchanges are largely dependent on fluctuations in the oil price in these countries. Oil price shocks are taken to be one variable that exerts a significant impact on their stock exchanges. In addition, it is assumed that the exchange rate and inflation can also influence the stock exchanges. Hence, the main question is how the oil price shocks, exchange rates, inflation and industrial production influence the stock markets. If there are significant impacts on stock exchanges, which variables show the strongest effects and how great are the impacts in these countries? Furthermore, as it is assumed that the oil price can have a significant impact on stock exchanges, then what about the impact of positive or negative shocks? In other words, which shocks have the stronger impacts on stock exchanges? This work seeks to answer these questions.

1. Introduction

2. Characterization of wind energy

3. Problem formulation

4. Exchange market algorithm (EMA)

5. Application of the EMA method for solving the optimization problem

6. Numerical results

7. Conclusion

References

Abstract

This paper presents an Exchange Market Algorithm (stocktickerEMA) method for solving the Economic Emission Dispatch (EED) problem including wind farms in the power systems. The stocktickerEMA algorithm is a powerful and useful method for finding the optimal value of an optimization problem with high accuracy. In recent years, because of the emission of harmful gases from fossil fuels and global warming issues, the penetration level of cleaner energies such as the wind and solar energy has been increased in order to produce the desired electrical energy. Therefore, it is vital to consider the wind turbines and wind farms in the EED optimization problem. Due to the probabilistic nature of wind speed in wind turbines, the generated power by wind turbines and wind farms has uncertain nature. Hence, the Weibull probability distribution function is used to model the wind power in the EED problem. The proposed method is tested on the IEEE 40-units test system. The analysis shows that, compared to other algorithms the EMA method has faster convergence and better ability in finding the optimal solution for the EED problem.

Introduction

Economic dispatch (ED) problem is very important for the economical operation of power systems. The purpose of the ED problem is to determine the optimal output values for the generation units to fulfill the load demand and other constraints of a power system, as well as minimizing the cost of electrical energy produced [1]. In the recent years the diffusion of harmful gases such as sulfur oxides (SOX ) and nitrogen oxides (NOX ), which pollutes the atmosphere and exacerbates the global warming situation, has become a critical issue. The main source of these greenhouse gases are the thermal units. One way to limit the emission of these gasses is to impose stricter policies on the thermal units. To apply the new regulations and tax issues for excessive generated greenhouse gases, a combination of economic dispatch and constraints on emission has been introduced which is known as Economic Emission Dispatch (EED) problem. In the EED optimization problem, in addition to minimizing the cost of energy, minimization of the amount of emission has been considered as well [2]. EED is an optimization problem with the following two main goals [3]:

(1) Minimizing the fuel cost of thermal units.

(2) Minimizing the emission of harmful gases into the air.

1. Introduction

2. Literature review

3. Methodology of analysis

4. Empirical data and analysis

5. Conclusions

References

Abstract

In this study, structural vector autoregression (SVAR) models are used to analyze the asymmetric effects of external and internal economic factors on the exchange market pressure index (EMPI) in the Czech Republic, Iceland, Poland, and Russia. Along with the forecast error variance decompositions (FEVDs) of the SVAR models and slope-based asymmetry tests suggesting that there may be asymmetric effects on EMPI, impulse response functions (IRFs) of SVAR models for the Czech Republic and Poland indicated that improvements in the current account balance will reduce the exchange market pressure (EMP), whereas deterioration of the current account balance may create a more serious speculative attack potential in Iceland and Poland, due to the high ratio of debt to GDP. Changes in inflation may have significant asymmetric effects on the EMP; more precisely, it was found that high inflation and the interest rate environment will increase the pressure on the exchange rate in all four economies. My findings indicate that decreases in inflation rates did not reduce the EMPI in Iceland and Russia, whereas the decline in interest rates did not constitute the necessary environment for capital inflows in any of the four countries. It is also implied that, due to the changes in long-term interest rates, the formation of the expectation of depreciation of the home currency is easier than the formation of the expectation of appreciation. Furthermore, I reveal that credit conditions in the United States of America (USA) could affect global crisis expectations and thus have asymmetrical effects.

Introduction

In terms of analyzing the factors leading to variations in exchange rates, it has become critically important to study the role of international spillovers, especially after global financial crisis (GFC) and the quantitative easing policy of major central banks. In the post-crisis period, the implementation of economic policies both in developed and developing countries focused on improving their deteriorated macroeconomic economic conditions. Along with internal and external macroeconomic factors, the need for foreign funds and international capital flows has come the core of the analysis of exchange rate fluctuations, especially in developing countries. In this respect, it is critically important to understand the dynamics of the foreign exchange market to make more precise explanations about the effects of exchange rate changes in future periods. Herein, the exchange rate pressure index (EMPI) has been recognized as a leading indicator of the current state of the foreign exchange market and the future value of the exchange rate. According to Weymark (1995), exchange market pressure (EMP) measures the total excess demand for an exchange rate change that would have been required to remove this excess demand in the absence of foreign exchange intervention (FXI). Because FXIs and interest rate adjustments can be used as policy tools to decrease the EMP in developing countries, the definition of EMP has been modified. In particular, the EMPI can be defined as the summation of weighted changes in official reserves, nominal exchange rate, and interest rates (Jing, 2015). There have been many studies in the literature focusing on using different EMPI definitions and analyzing the macroeconomic and financial variables as determinants of the EMPI (Aizenman & Binici, 2016; Aizenman & Hutchison, 2012; Aizenman, Lee, & Sushko, 2012; Akram & Byrne, 2015; Feldkircher, Horvath, & Rusnak, 2014; Panday, 2015; Soe & Kakinaka, 2018). However, exchanges rates can interact with many domestic macroeconomic and financial variables, meaning that models analyzing the factors influencing EMP can be enhanced by the inclusion of domestic economic risk factors (Forbes, Hjortsoe, & Nenova, 2018). In this way, exogenous exchange rate shocks can be taken into consideration in the analysis of EMP. For this purpose, credit default swap (CDS) or the three-month LIBOR based on the US dollar minus three-month Treasury bill (TED spread), which is under the influence of both monetary and fiscal policy implementation, can be included in an empirical model as the main country risk indicators. Due to the spillover effects of the monetary policy of the US after the GFC, the inclusion of macroeconomic and financial variables of foreign countries in models examining the variations in exchange rates has also gained ground in the most recent scientific literature (Adler, Lama, & Medina, 2018; Belke & Fahrholz, 2018; Jordan, 2016).

1- Introduction

2- Empirical methodology

3- Data and variables measurement

4- Empirical results

5- Conclusion

References

Abstract

We examine the effect of an exogenous increase in information asymmetry (as proxied by late filings of firms’ Form 10-K) on bond prices. We find that bondholders react negatively to a late filing announcement but this negative reaction is conditional on whether late filing firms appropriate wealth from bondholders through shareholder distribution. Moreover, we find that the impact of financial distress and covenants on bond values is mainly driven by the wealth appropriation from bondholders. The results are robust to difference-in-difference analysis using treatment (i.e., late filing) and control (i.e., non-late-filing) samples based on propensity score matching. The results provide evidence that shareholder distribution as a specific form of wealth appropriation from bondholders to shareholders has a significant effect on bond values when financial information is not timely provided to capital markets.

Introduction

It is well known that timely disclosure of periodic financial statement information helps capital market participants make informed investment decisions, which in turn decreases the information asymmetry between managers and investors (e.g., Glosten & Milgrom, 1985; Hakansson, 1977; Healy & Palepu, 2001). One such vital corporate disclosure is the firm’s annual financial filings with the SEC, i.e., the Form 10-K.1 Recent research on the equity side examining the consequences of an increase in information asymmetry caused by late disclosures of 10-Ks documents negative and significant equity market reaction (e.g., Bartov, Defond, & Konchitchki, 2015).2 In this paper, we investigate the effect of an exogenous increase to information asymmetry, as proxied by late filing of annual financial statements with the SEC, on bond values.3 We focus on the U.S. bond market for several reasons. First, the U.S. bond market is one of the largest capital markets in the world with over $9.8 trillion outstanding corporate bonds issued by U.S. firms as of end of 2013.4 Second, prior research suggests that the value of debt is less sensitive to asymmetric information than the value of equity because debtholders own a put option on the firm’s assets (e.g., Kerr & Ozel, 2015; Myers & Majluf, 1984). Hence, it is unclear whether the asymmetric information caused by late disclosures will have significant consequences in the bond market. Third, the U.S. bond market is dominated by institutional investors, and it is unclear whether bond investors will react to announcements of non-timely SEC filings if these institutional investors can have access to various information sources other than public financial filings (e.g., Defond & Zhang, 2014; Ronen & Zhou, 2013) that can help them anticipate the late filing before it actually occurs. Fourth, announcements of non-timely filings of financial statements may not necessarily trigger bond values downward because late filing firms may agree with bond investors to delay the release of their financial statements by, for example, giving bondholders more leverage over the firm’s assets.5 Finally, prior research finds that late filing firms are on average smaller, more levered, with lower market to book and profitability (e.g., Bartov et al., 2015). These firms are usually in serious default risk and bondholders may have already priced in such a high level of information asymmetry. Therefore, the findings on the consequences of an increase in information asymmetry caused by late filings previously documented in the equity market may not necessarily hold in the bond market.

1. Introduction

2. Dataset

3. Research method and data analysis

4. Discussion

5. Conclusion

Acknowledgments

Appendix.

References

Abstract

This research examines the foreign exchange (FX) market from the perspective of currency network. We construct the network based on correlations between exchange rates of 37 currencies from 2006 to 2012. The minimum spanning tree (MST) is used to generate a simplified network and bootstrap technique is employed to test the reliability of links. The full correlation matrices are further analyzed to support and test the robustness of the results from the MSTs. Specifically, we compare the results in the pre-crisis period (2006-2007) and the post-crisis period (2011-2012) to show the impact of the 2008 global financial crisis on the FX market. We have the following findings: (a) the currency network is more scattered in the post-crisis period; (b) the connections between currencies tend to be more internal within the geographic region after the crisis; (c) the European currencies maintain strong connections and keep their clustering feature stable.

Introduction

The process of globalization is closely linking financial markets worldwide, leading to the formation of economic networks. As one of the most important markets in the global financial system, the foreign exchange (FX) market has experienced a large increase in recent decades. According to the triennial survey conducted by Bank for International Settlements (BIS), global FX turnover averaged $5.1 trillion per day in 2016, down from $5.4 trillion in 2013 for the appreciation of the US dollar, but up with a 27.5% increase by comparison with $4.0 trillion in 2010 [1].

The FX market has received attention from several disciplines such as economics, physics and systems science. The network approach from systems science and econophysics seems to provide a new paradigm to touch the complexity of the FX market with the interaction of world currencies. From the network perspective of the FX market, the currencies (or exchange rates) are considered as nodes and the pairwise correlations between them as the links, and thus a similarity based network can be constructed

1. Introduction

2. Case study

3. Applied deep learning architectures

4. Methodology

5. Results

6. Conclusions

Appendix

References

Abstract

We present the implementation of a short-term forecasting system of price movements in exchange markets using market depth data and a systematic procedure to enable a fully automated trading system. Three types of Deep Learning (DL) Neural Network (NN) methodologies are trained and tested: Deep NN Classifier (DNNC), Long Short-Term Memory (LSTM) and Convolutional NN (CNN). Although the LSTM is more suitable for multivariate time series analysis from a theoretical point of view, test results indicate that the CNN has on average the best predictive power in the case study under analysis, which is the UK to Win Horse Racing market during pre-live stage in the world’s most relevant betting exchange. Implications from the generalized use of automated trading systems in betting exchange markets are discussed.

Introduction

The increasing amount of data reveals that the Big Data era is here to stay and constitutes a new form of strategic behavior and business interaction. Data is currently considered one of the most valuable intangible assets in the world. The domain of data analytical techniques is a key step not only to facilitate the transformation and growth of firms but also to boost the level of digital literacy. Goodfellow et al. (2016) recognize that the use of Deep Learning (DL) constitutes an enabler of disruptive change for businesses due to its power of association, regression, classification and clustering. Machine learning incorporates a vast array of algorithmic implementations, which not all of them can be classified as DL. Indeed, the later only corresponds to a subset of the former field of research.

Historically emerging from cognitive and information theories, DL aims at imitating the learning process of human neurons and creates complex interconnected neuronal structures sim-ilar to human synapses. Hence, DL consists of the application of multi-neuron, multi-layer Neural Networks (NN) to perform learning tasks such as regression, classification, clustering or encoding/decoding. The ability for a NN to be used in a wide variety of data and learn indiscriminately implies that the DL approach can be applied to a considerable number of case studies rather than requiring the development of a structure for each new analysis. Varian (2014) recognizes the relevance of DL NN architectures for the economics field. Proficiency with data mining, data visualization tools and artificial intelligence rank as one of the most important skills in determining business success, thus, any effort to educate stakeholders is clearly advised.

JEL classification

1. Introduction

2. Related literature

3. Sample and summary statistics

4. Methodology

5. Empirical results

6. Discussion

7. Conclusions

Appendix. Supplementary materials

References

Abstract

Since Saunders (1993), there has been ongoing research on whether the weather can affect asset prices. Our study of the impact of weather on stock and foreign exchange (henceforth FX) markets shows that the weather has no effect during 2002–2018, suggesting that any effect may have dissipated after discovery as practitioners have tried to use investment vehicles to exploit it. The results indicate that mood changes caused by the weather do not significantly affect FX and stock returns.

Introduction

Behavioral finance holds that security market prices are determined not only by their intrinsic values but also by investor psychology. Weather can affect investors’ moods and thus their behavior in financial markets. For example, investors may be in a good (bad) mood when it is sunny (during thunderstorms), so they (do not) buy stocks (Daniel et al., 1998).

If the weather affects investor mood, resulting in stock price fluctuations, could the weather also affect the FX market? The exchange rate is affected by many factors, like inflation and interest rates. In the short term, it may be affected by events like Brexit. Will FX participants be affected by the weather in their trading decisions?

We investigate whether the weather affects FX markets. We select nine currencies: US Dollar (USD), Japanese Yen (JPY), Euro (EUR), Pound (GBP), Australian Dollar (AUD), Canadian Dollar (CAD), Swiss Franc (CHF), New Zealand Dollar (NZD) and Swedish Krona (SEK), respectively. We include eight currency pairs in our study, pairing the various currencies against USD and GBP, respectively. To compare the effects of the weather on the FX and stock markets, we also review US and UK stock indices. We find insufficient evidence that the weather can affect the stock and FX markets in the US and the UK. Although the weather can affect a person’s mood, this does not mean that it can affect asset prices.

The next section reviews the relevant literature. Section 3 describes the weather and return data for the FX and stock markets. Section 4 details the methodology. Section 5 offers the results and Section 6 discusses their implications. The final section concludes.

1. Introduction

2. Data and methodology

3. Empirical results

4. Conclusion

Appendix. Descriptive statistics, regime classifications and profitability of arbitrage.

References

Abstract

In this study, we investigate monthly seasonality in the foreign exchange market. Given the well-known recurrent higher returns in some month than in others in stock markets around the world, we consider it likely that a seasonal outperformance of a country’s stock market over another is associated with similar seasonal patterns in capital flows and exchange rates. A seasonal profit (carry trade) opportunity can be created by the simultaneous appreciation of a country’s currency and the outperformance of its stock market. By focusing on the world’s key currency pairs, the US dollar-Deutsche mark and the US dollar-euro, and by using a Markov-switching framework, we document persistent January and December effects in the foreign exchange market from 1971 to 2017. Analysis of the German-US stock returns differential and their bilateral capital flows reveal similar month effects in 65% of the whole sample.

Introduction

A rarely explored feature of exchange rates is their persistent monthly seasonality. According to the informationally-efficient market hypothesis (Fama, 1970), calendar regularities, such as higher foreign exchange gains in a specific month, should already be included in asset prices. Profit opportunities associated with such regularities represent calendar anomalies and violate informational efficiency (Fama, 1970). The persistence of such a violation should not be surprising in light of the persistent January effect in most stock markets, which has still not been arbitraged away. However, it is difficult to understand why researchers of foreign currency markets have never related their findings to those of stock market experts. Indeed, since there is only limited evidence of seasonality in bond yields1 for old samples, the January effect in the returns differential between two countries’ stock markets is the main candidate to rationalize the similar seasonality in their currency pair. The natural conduit between these markets is seasonal equity capital flows, the engine of equity carry trades.2 Such relationships have been studied at a general level in the literature on uncovered equity parity,3 but no attention has been granted to their possible seasonal character. The reason why known seasonalities in these markets are not fully arbitraged away is that they take place most of the time, but not all the time. In other words, they are non-linear, occurring in some regimes but not in others.

We pursue three objectives in this study. First, we aim to revisit the presence of monthly seasonality in the foreign exchange market and its non-linear character. Second, we examine the monthly seasonal behavior of the corresponding stock returns differential in a similar non-linear framework. We gauge the synchronicity and the similarity of the seasonal patterns of the exchange rate returns and the stock returns differential. Third, to explore the transmission channel of equity carry trade opportunities, we investigate whether this seasonal synchronicity is reflected in the seasonal pattern of the bilateral equity flows.

Abstract

Purpose – There is an existing relationship among shareholders, boards of directors and management of companies. Corporate governance practices of companies are expected to ensure that this relationship maximises the wealth of shareholders. Differences exist among corporate governance of companies listed on the Ghana Stock Exchange. Companies, for purposes of liquidity, hold cash, but cash holdings also add to the cost of financing, according to working capital theories. The study, thus, sought to examine the relationship between corporate governance practices, ownership structure, cash holdings and firm value. Design/methodology/approach – The study deployed the seemingly unrelated regression to reduce the problem of multicollinearity resulting from the strong relationship between cash reserves and some control variables. Findings – The study found no significant relationship between board size and firm value. Similar findings were also made on the relationship between proportion of non-executive directors on the board and firm value. However, firms audited by the big four audit firms are valued higher by the capital market. Cash holdings of firms negatively affect performance, and this is statistically significant. A positive relationship arises between a firm’s cash holdings and its value as a result of debt financing, even though this is not significant. Originality/value – The study is the first of its kind that deploys Tobin’s Q as a measure of firms’ value to reflect investors’ valuation of firms in Ghana. The study is also the first of its kind to test the interactive effect of debt financing and cash holdings on firm value in Ghana.

Introduction

The value of a company is affected by factors within the control of management and other external factors. Valuation models identify the value of a company as a function of the risk faced by the company. Risk, in the literature, is identified as systematic and unsystematic risk, where systematic risk refers to risk arising from market-related factors, and unsystematic risk arises from entity-specific factors. Entity-specific factors, such as cash holding, corporate governance and ownership structure, can influence a company’s value (Harford et al., 2008). In valuation of equity shares, one approach identified in the literature is the free cash flow method, which values shares based on the free cash flow available. This means that the amount of cash a company holds at any point in time has influence on its value (Isshaq et al., 2009). Corporate governance, the mechanism within which a firm operates and is directed, can also influence its value. Jensen (1993) identified that as a result of the agency problem, the shareholder wealth maximisation goal, which managers are to pursue, is disregarded for personal goals, such as increasing their perks. This phenomenon is not value-adding. Eberhart (2012), in a research based in Japan, showed that companies that adopted the Anglo-American committee system experienced increase in their firm valuation after the adoption, and the reason stated was that the adoption signalled a move to transparency. With transparency, asymmetric agency cost was also expected to be reduced. Other researchers, such as Stiglbauer and Velte (2014), show that soft law regulations governing corporate governance have no impact on the value of firms. The effect of executive ownership on different economic variables has been assessed extensively in current literature (Iona et al., 2017; Iona and Leonida, 2016; Ehikioya, 2009).

Purpose – In the process of discussing the relationship between volume and price in the stock market, the purpose of this paper is to consider how to take the flow of foreign capital into consideration, to determine whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price. Design/methodology/approach – By comparing the relative advantages and disadvantages of the two main non-parametric methods mainstream, and taking the characteristics of the time series of the volume into consideration, the stochastic volatility with Volume (SV-VOL) model based on the APF-LW simulation method is used in the end, to explore and implement a more efficient estimation algorithm. And the volume is incorporated into the model for submersible quantization, by which the problem of insufficient use of volume information in previous research has been solved, which means that the development of the SV model is realized. Findings – Through the Sequential Monte Carlo (SMC) algorithm, the effective estimation of the SV-VOL model is realized by programming. It is found that the stock market volume information is helpful to the prediction of the volatility of the stock price. The exchange market volume information affects the stock returns and the price-volume relationship, which is achieved indirectly through the net capital into stock market. The current exchange devaluation and fluctuation are not conducive to the restoration and recovery of the stock market. Research limitations/implications – It is still in the exploratory stage that whether the inclusion of volume information really contributes to the prediction of the volatility of the stock price, and how to incorporate the exchange market volume information. This paper tries to determine the information weight of the exchange market volume according to the direct and indirect channels from the perspective of causality. The relevant practices and conclusions need to be tested and perfected. Practical implications – Previous studies have neglected the influence of the information contained in the exchange market volume on the volatility of stock prices. To a certain extent, this research makes a useful supplement to the existing research, especially in the aspects of research problems, research paradigms, research methods and research conclusion. Originality/value – SV model with volume information can not only effectively solve the inefficiency of information use problem contained in volume in traditional practice, but also further improve the estimation accuracy of the model by introducing the exchange market volume information into the model through weighted processing, which is a useful supplement to the existing literature. The SMC algorithm realized by programming is helpful to the further advancement and development of non-parametric algorithms. And this paper has made a useful attempt to determine the weight of the exchange market volume information, and some useful conclusions are drawn.

Introduction

The managed floating exchange rate system has been put into action in China since July 21, 2005. Facing the abnormal fluctuation (represented by abrupt appearance and disappearance of circuit-breaker mechanism) in the stock market at the beginning of 2016 and under the background that RMB has showed the depreciation trend upon being added to SDR, it is essential to delve deeper into the issue whether the stock market fluctuation is buffered to some extent as a result of capital control for its restriction of capital flight, or aggravated for the gambling operation of the foregoing capital in the stock market. When exploring the stock price-volume relationship in the stock market, this dissertation makes tentative efforts to take the intention or strength of capital flight into consideration. In the foreign exchange market, capital flight will lead to the supply demand change of RMB and eventually demonstrate as an important factor of exchange rate fluctuation. Based on the analytical framework of stock price-volume relationship, the spot inquiry of financial institutions can represent more directly the intention and strength of capital flight in the foreign exchange market. With this, the spot inquiry volume of foreign exchange, as the direct representation, ranks to be a more targeted, direct and effective proxy variable compared with the exchange rate volatility. Therefore, this dissertation attempts to judge the forecast function of volume on stock price fluctuation by integrating foreign exchange volume information into the analytical framework of stock price-volume relationship, and to identify the external effect of capital control on stock market volatility, so as to provide experience reference for the existing policy adjustment in Chinese stock and foreign exchange market by virtue of research design and conclusions.