Stock Return Rate Research Articles

Causal Nexus between Stock Price, Demand for Money, Interest Rate, Foreign Institutional Investment, and Exchange Rates in India: A Post Subprime Crisis Analysis

This paper makes an attempt to empirically examine the causal nexus between stock price, demand for money, interest rates, foreign institutional investment and exchange rates in India in the post subprime mortgage crisis period. The study employed Granger causality test, Vector Auto Regression and Johansen Maximum Likelihood procedure to examine the short run and long run dynamic interaction among the above mentioned variables for the period January 1993 to May 2009. The major indings of the study are: stock return affects exchange rate return, net foreign institutional investment and growth of demand for money. Growth of demand for money, in turn, affects interest rate. Interest rate is more affected by exchange rate return. Foreign institutional investment also affects interest rate. The co-integration test conirms that there does not exist any long run equilibrium relationship between stock return and exchange rate return );} // --> activate javascript

기업의 SNS 노출과 주식 수익률간의 관계 분석

Can the stock market really be predicted? Stock market prediction has attracted much attention from many fields including business, economics, statistics, and mathematics. Early research on stock market pre-diction was based on random walk theory (RWT) and the efficient market hypothesis (EMH). According to the EMH, stock market are largely driven by new information rather than present and past prices. Since it is unpredictable, stock market will follow a random walk. Even though these theories, Schumaker [2010] asserted that people keep trying to predict the stock market by using artificial intelligence, statistical estimates, and mathematical models. Mathematical approaches include Percolation Methods, Log-Periodic Oscillations and Wavelet Transforms to model future prices. Examples of artificial intelligence approaches that deals with optimization and machine learning are Genetic Algorithms, Support Vector Machines (SVM) and Neural Networks. Statistical approaches typically predicts the future by using past stock market data.Recently, financial engineers have started to predict the stock prices movement pattern by using the SNS data. SNS is the place where peoples opinions and ideas are freely flow and affect others’ beliefs on certain things. Through word-of-mouth in SNS, people share product usage experiences, subjective feelings, and commonly accompanying sentiment or mood with others. An increasing number of empirical analyses of sentiment and mood are based on textual collections of public user generated data on the web. The Opinion mining is one domain of the data mining fields extracting public opinions exposed in SNS by utilizing data mining. There have been many studies on the issues of opinion mining from Web sources such as product reviews, forum posts and blogs. In relation to this literatures, we are trying to understand the effects of SNS exposures of firms on stock prices in Korea. Similarly to Bollen et al. [2011], we empirically analyze the impact of SNS exposures on stock return rates. We use Social Metrics by Daum Soft, an SNS big data analysis company in Korea. Social Metrics provides trends and public opinions in Twitter and blogs by using natural language process and analysis tools. It collects the sentences circulated in the Twitter in real time, and breaks down these sentences into the word units and then extracts keywords. In this study, we classify firms’ exposures in SNS into two groups: positive and negative. To test the correlation and causation relationship between SNS exposures and stock price returns, we first collect 252 firms’stock prices and KRX100 index in the Korea Stock Exchange (KRX) from May 25, 2012 to September 1, 2012. We also gather the public attitudes (positive, negative) about these firms from Social Metrics over the same period of time. We conduct regression analysis between stock prices and the number of SNS exposures. Having checked the correlation between the two variables, we perform Granger causality test to see the causation direction between the two variables. The research result is that the number of total SNS exposures is positively related with stock market returns. The number of positive mentions of has also positive relationship with stock market returns. Contrarily, the number of negative mentions has negative relationship with stock market returns, but this relationship is statisti-cally not significant. This means that the impact of positive mentions is statistically bigger than the impact of negative mentions. We also investigate whether the impacts are moderated by industry type and firm’s size. We find that the SNS exposures impacts are bigger for IT firms than for non-IT firms, and bigger for small sized firms than for large sized firms. The results of Granger causality test shows change of stock price return is caused by SNS exposures, while the causation of the other way round is not significant. Therefore the correlation relationship between SNS exposures and stock prices has uni-direction causality. The more a firm is exposed in SNS, the more is the stock price likely to increase, while stock price changes may not cause more SNS mentions

A Study on the Relation between Stock Return and Exchange Rates : Internationalization and Market Difference

A Study on the Relation between Stock Return and Exchange Rates : Internationalization and Market Difference

기업의 동반성장 노력이 재무성과에 미치는 영향

In this study, we empirically examine the impact of win-win growth effort of domestic large firms on their financial performance. Specifically, we classify the financial performance into three aspects such as profitability, stability and efficiency, select corresponding financial ratios to each aspect, and analyze the causal relationship between the firms' win-win growth effort and each of the financial ratios. In addition, we figure out the impact of the firms' win-win growth effort on their stock rate of return. From the analysis, we show that the win-win growth effort has a positive impact on the firms' profitability, stability and stock prices; however, it does not give statistically significant impact on the firms' efficiency with even negative impact on it. These results imply that the firms' win-win growth effort could bring about inefficiency in their business operations, but the effort could increase the firms' profitability and make their financial structure more stable. Furthermore, the effort could enhance the firms' image of leading CSR (corporate social responsibility), which in turn increase their stock values.

An optimal investment model with Markov-driven volatilities

We consider a multi-stock market model. The processes of stock prices are governed by stochastic differential equations with stock return rates and volatilities driven by a finite-state Markov process. Each volatility is also disturbed by a Brownian motion; more exactly, it follows a Markov-driven Ornstein–Uhlenbeck process. Investors can observe the stock prices only. Both the underlying Brownian motion and the Markov process are unobservable. We study a discretized version, which is a discrete-time hidden Markov process. The objective is to control trading at each time step to maximize an expected utility function of terminal wealth. Exploiting dynamic programming techniques, we derive an approximate optimal trading strategy that results in an expected utility function close to the optimal value function. Necessary filtering and forecasting techniques are developed to compute the near-optimal trading strategy.

Interaction between Stock and Foreign Exchange Markets Under the Floating Regime: Evidence from Thailand

This paper explores the nature of the linkage between foreign exchange market and the Thai stock market using monthly data of exchange rate and stock prices during July 1997 to June 2010. The results from cointegration test in a bivariate framework show no long-run relationship between stock prices and nominal effective exchange rate. The noncausality test is thus employed to investigate the existence and direction of causality. It is found that there exists positive bidirectional causality between nominal effective exchange rate and stock prices. This evidence supports the flow-oriented theory, which indicates a positive linkage between stock prices and exchange rate. In addition, the results from a bivariate constant conditional correlation autoregressive conditional heteroskedastic model and the standard Granger causality test in multivariate framework indicate the existence of bidirectional causality between stock return and exchange rate return. Additionally, exchange rate return volatility does not cause stock return to decrease.

A Nonlinear Dynamic Model of the Financial Crises Contagions

Employing the Differential Dynamics Method, a nonlinear dynamic model is set up to describe the international financial crises contagion within a short time between two countries. The two countries’ control force depending on the timely financial assistance, the positive attitude and actions to rescue other infected countries, and investor confidence aggregation, and the immunity ability of the infected country are considered as the major reasons to drive the nonlinear fluctuations of the stock return rates in both countries during the crisis. According to the Ordinary Differential Equations Qualitative Theory, we found that there are three cases of financial crises contagion within a brief time between two countries: weak contagion with instability but inhibition, contagion with limit and controllable oscillation, and strong contagion without control in a brief time.

Identifying the effects of a lender of last resort on financial markets: Lessons from the founding of the fed

We use the founding of the Federal Reserve to identify the effects of a lender of last resort. We examine stock return and interest rate volatility during September and October, when markets were vulnerable because of financial stringency from the harvest. Stock volatility fell by 40% and interest rate volatility by more than 70% following the monetary regime change. The drop is insignificant if major panic years are omitted from the analysis, however. Because business cycle downturns occurred in the same year as financial crises, our results suggest that the existence of the Federal Reserve reduced liquidity risk.

Are There Smaller Leverage Effects in Less-Developed Markets? Evidence from an Oil Exporting Country

Problem statement: This study uses daily data from the Tehran Stock Market (TSM) to illustrate the nature of stock market volatility in an undeveloped and young stock market. Although most studies suggest that a negative shock to stock prices will generate more volatility than a positive shock of equal magnitude but there is no evidence of asymmetric effect in TSM. Determine the nature of stock market volatility in an oil exporting country. Approach: Trading in Tehran Stock Market (TSM) is based on orders sent by the brokers. The data consist of 2375 daily observations of the closing value of the Tehran stock market from 3/30/1998 to 5/04/2007. Our empirical finding shows that the unconditional variance is 0.18 but visual inspections of the time series suggests that volatility of the stock return rate displays the clustering phenomenon associated with GARCH processes. Results: The estimation and test results for all models suggest that the leverage effect term, γ, is not significant at 5% level. Although, in Asym. CARCH model based on normal distribution for errors, the estimated coefficient on the asymmetry term is -0.066 with a z-statistics of -1.749 recognized as significant at 10% level, but it has the wrong sign. It seems that good news and bad news has the same effect on stock prices in TSM, a result that is contradictory to other studies for developed countries. Conclusion: The estimated models containing TARCH, EGARCH, asymmetric CARCH and PARCH with different assumptions on error distributions suggest no strong and significant asymmetric effect. There are some reasons for this finding: (1) In Iran with Islamic laws, debt contracts are illegal or at least not enforced and Iranian firms do not have any financial leverage. As a result, we would expect to find smaller leverage effects in volatility in Iran than in the United States, for example. In deed the institutional differences with western financial markets manifest themselves in different return characteristics. (2) Stock prices in the TSM by regulation and intervention cannot exceed from some range. The strong serial correlation in returns necessitating long lags in the mean equations is possibly due to such regulations. (3) The history of TSM is very short compared to other stock markets and the information flow in this market is very slow. The estimated coefficients on the expected risk (as a measure of the risk-return tradeoff) are not significant. These findings suggest that the TSM is not efficient.

Integration and Efficiency of Stock and Foreign Exchange Markets in India

This article attempts to examine the integration and efficiency of Indian stock and foreign exchange markets. The study employed Time series ordinary least square regression, Unit Root test, Grangers causality test, Vector Auto Regression techniques on monthly data of stock return and exchange rate return for the period spanning from February 1995 to March 2005.The major finding of this study are as follows. Both the stock indices return (Rsensex and Rnifty) are near normal whereas exchange rate return is not normal and more peak. The stock return and exchange rate return are positively related. The policy implication of this above result of the positive relation between stock return and exchange rate return for the foreign investors in India should be further studied. From the Granger's causality test, it is found that there is no causality for the return series of stock indices and exchange rate except return Nifty and return exchange rate. Weak form of market efficiency hypothesis is also corroborated for stock and foreign exchange markets.

Stock Return and Interest Rate Risk at Fannie Mae and Freddie Mac

Stock Return and Interest Rate Risk at Fannie Mae and Freddie Mac

Stock Return and Interest Rate Risk at Fannie Mae and Freddie Mac

Fannie Mae and Freddie Mac are government-sponsored enterprises (GSEs) with the stated objective of promoting home ownership by improving the availability of mortgage financing for private households. These enterprises engage in two separate and distinct lines of business: (i) assembling and marketing pools of mortgages on which they guarantee the timely payments of principal and interest and (ii) purchasing mortgage assets for their own portfolio, mostly funded with debt securities. This article examines the sensitivity of the returns on GSEs' equity shares to realizations of interest rate risk. 

Long Term Bond Markets and Investor Welfare

A simple framework is developed to analyze the important role of the bond market for the welfare of a long horizon small investor when both inflation and the real interest rate are stochastic. Closed form solutions for investor utility in different settings are used to show that the welfare loss due to the absence of long term bond markets increases with the investor's horizon and risk aversion, and with the size and persistence of the shocks to the real interest rate, but decreases with the correlation between the stock return and real interest rate. Using parameter estimates derived from U.S. data, we show that the welfare costs of the lack of a bond market can be substantial.