Pockets of Predictability

Purpose- This study aims to examine the effect of effective spread, price impact, trading volume, stock prices, and volatility of returns on the predictability of short-term returns (short horizon return predictability).
 Methods- This research offers a new approach perspective which is a market microstructure with intraday data to measure short horizon return predictability as an efficient market inversion. The sample in this study was 64 non-financial companies listed on the KOMPAS100 Index during October 2017-March 2018. Intraday data used using the 5-minute frequency obtained from Bloomberg. This study uses multiple linear regression analysis.
 Finding- This study found that price impact, trading volume, stock prices, and volatility have a positive impact on the predictability of long-term returns. This study also found that effective spread does not have a significant impact on the predictability of short-term returns.

Growth to Value: A Difficult Journey for Ipos and Concentrated Industries

PurposeConsidering the relationship between the central bank balance sheet and unconventional monetary policy after the 2008 financial crisis, it is crucial to see how the unconventional monetary policy, given near-zero interest rates, affects future stock market performance. This paper analyzes the impact of the Fed's balance sheet size on stock market performance.Design/methodology/approachTo analyze the Fed's balance sheet size's long-term stock market implications, this paper uses the asset pricing framework of market return predictability such as Ordinary least squares (OLS) and Generalized method of moments (GMM) analysis.FindingsFindings in this paper suggest that the Fed's balance sheet size, deflated by asset market wealth, presents evidence of return predictability during 1926–2015 that is robust against standard controls. These results can be explained through the redistribution of risk and the wealth channels of monetary policy transmission. The changing balance sheet size of a central bank (1) affects systemic risk, yields and expectations and (2) signals the future direction of monetary policy and thus economic outlook.Research limitations/implicationsThe main implication of these findings is that policymakers should avoid a severe imbalance between a central bank's balance sheet size and assets market wealth.Originality/valueThe empirical evidence in this paper documents a century-old relation between the Fed's balance sheet size and US stock market return using the Fed's balance sheet data for the last 100 years and stock market returns from the Center for research in security prices (CRSP) database.

Missing data problem is ubiquitous in many real life situations. Information Technology researchers have explored and tried to address this problem in different settings. In this thesis, we undertake research to address missing data problem associated with order book information in stock markets. This is an in-depth and large-scale study with systematic and comprehensive framework to address missing data problem in the finance literature. Orders placed by traders and the corresponding order imbalance (OIB) is informative to predict future stock returns, however, stock exchange rules do not reveal price sensitive complete order book data for traders. Hence, return prediction using the revealed, incomplete trade book data (that contains only matched buy and sell orders and deletes the unmatched orders), does not let traders to completely exploit possible short term trading opportunities. Hence, this can be considered as a classical missing data problem for predicting future returns, by using the information content of order book. This thesis addresses the missing data problem by developing an integrated theoretical framework applied in stock market trading environment. We use relational Markov networks theory and build an empirically testable Algorithm for Imputed Complete Order Book (AICOB). The thesis contributes by developing a new theoretical advancement of information technology research relating to missing data problem and applying it to financial markets. First, the thesis presents the missing data problem as a Missing at Random (MAR) data and builds a systematic framework to estimate single as well as joint log likelihood functions. The thesis demonstrates that estimating by using incomplete records, improves the accuracy of the parameter estimates. Second, the thesis proposes a Relational Markov Network Model for estimation of the joint distribution function of orders, order characteristics and their interactions. Later, the Expectation Maximization Algorithm is proposed to address the missing data problem during the joint estimation procedure. All pooled regression results follow Fama and MacBeth (1973) and Generalized Methods of Moments (GMM) methods. These methods control the cross sectional and time series correlations between the observations and across the pooled stocks. The proposed novel methodology overcomes the estimation problem in the context of missing order book data. Third, the thesis develops an objective evaluation strategy for AICOB, based on efficiency, accuracy and adaptability dimensions. The thesis uses Australian stock market data, which provides not only trade book data but also historical order book data for cross validating the results. This unique setting allows validating the accuracy of AICOB methodology by comparing with complete order book data. The main contribution of the thesis is to show that AICOB based predictions match with the complete order book data. Whereas, trade book based predictions are quite inconsistent to the complete order book data. The AICOB based results are also consistent with the theoretical predictions proposed in finance literature that OIB predicts better as the firm size increases. The results show that large firms, with higher trading activity and more competition for order flows, report more significant OIB prediction of future returns. Trade Book based OIB estimates, which suffer from missing data problem, fail to predict future returns for stock portfolios. Hence, addressing the missing data problem is important before implementing OIB based trading strategies. Overall, the thesis finds that machine learning applications, similar to AICOB, can be helpful in implementing the trading strategies in financial markets. The imputation of missing data, through systematic procedures, based on theoretical distributional properties of the financial variables, can be informative for more accurate prediction of future returns. The thesis contributes towards establishing a common ground for cross-disciplinary research in Finance and Information Technology (IT) by applying the advances in IT research to solve research and corresponding implementation problems in finance. Further, the thesis contributes towards advancing the order imbalance literature by showing that missing data can play a critical role in the predictive ability of order imbalance.

In this study, I test the predictability of stock market excess returns with households’ obligations ratio. Using U.S stock market data, I show that household’s debt service ratio can predict stock market returns at short horizon and over business cycle frequencies. I show that between 1980 and 2016, mean deviations from debt service ratio is a better forecaster of future returns both in-sample and out-of-sample than dividend-price ratio, dividend yield, earnings-price ratio, investment-capital ratio, and several other popular forecasting variables. The results remain significant using quarterly data and annual data.

Predictability in financial markets: What do survey expectations tell us?

Recent financial research has provided evidence on the predictability of asset returns. In this paper we consider the results contained in Pesaran and Timmerman (1995), which provided evidence on predictability of excess returns in the US stock market over the sample 1959–1992. We show that the extension of the sample to the nineties weakens considerably the statistical and economic significance of the predictability of stock returns based on earlier data. We propose an extension of their framework, based on the explicit consideration of model uncertainty under rich parameterizations for the predictive models. We propose a novel methodology to deal with model uncertainty based on ‘thick’ modelling, i.e. on considering a multiplicity of predictive models rather than a single predictive model. We show that portfolio allocations based on a thick modelling strategy systematically outperform thin modelling. Copyright © 2005 John Wiley & Sons, Ltd.

The long and the short of the risk-return trade-off

This study investigates whether the influence of financial expertise on stock investors’ ability to predict risk and return is contingent on the length of the forecast horizon. In a quasi-experimental design, stock market professionals (N1=63, N2=36), private shareholders (N1=155, N2=172) and students (N1=124, N2=90) twice provided their short- (3-month) and long-term (2-year) risk and return predictions on stock indices. The results show that in general, experts did not outperform students or private shareholders in their return predictions. However, the level of financial expertise positively influenced the accuracy of risk predictions. An interaction effect between financial expertise and the length of the forecast horizon suggests that more knowledgeable and experienced investors performed better in the long term compared to the short term than inexperienced investors did

Summary/Zusammenfassung Zur Rendite-Risiko-Beziehung am deutschen Aktienmarkt Eine empirische Analyse der Beziehung zwischen dem Deutschen Aktienindex DAX und dem Volatilitatsindex VDAX Diese Studie untersucht den empirischen Zusammenhang zwischen den Volatilitatsindizes VDAX bzw. VDAX-New und dem Aktienmarktindex DAX. Der im internationalen Umfeld dokumentierte negative Zusammenhang zwischen impliziten Volatilitatsindex- und Aktienmarktindexrenditen kann auch am deutschen Aktienmarkt nachgewiesen werden, wobei der negative Zusammenhang im Falle negativer DAX-Renditen betragsmasig starker ausgepragt ist als bei positiven DAX-Renditen. Neben dieser Asymmetrie konnen zusatzliche quadratische Effekte, die sich als Angst bzw. Uberschwang interpretieren lassen, nicht beobachtet werden. Die empirischen Ergebnisse sind robust hinsichtlich des gewahlten Volatilitatsindexes, der Datenperiodizitat sowie des Untersuchungszeitraums und lassen sich mit Erkenntnissen aus der Behavioral-Finance-Forschung erklaren. Es er...

The need for stock market is rising in the modern day. Particularly, there is growing interest in companies with high share prices. However, the danger also rises as the interest does. There are several reasons for this. In other words, inflation is one of the largest risk concerns. Inflation control is a national issue, not just a business issue. Research on this subject was done by scientists including Thomas C. Chiang, Amal Essayem Sakir, Gormus Murat Guven, and Pierlauro Lopez. However, they haven't done enough study on how to predict inflation and how much it affects the stock market. The stock market, inflation, and the predictability of stock market returns are all topics that are clarified in this article.

<p class="MsoNormal" style="text-indent: 0in; margin: 0in 0.5in 0pt; mso-pagination: none;"><span style="font-family: "Times New Roman","serif"; font-size: 10pt;" lang="EN-GB">This paper makes use of time-varying parameter GARCH-M model to estimate the risk aversion parameter for the South African stock market. The paper further compares the forecasts performance of a time-varying risk premium model with that of a constant risk premium model in predicting stock market returns on the South African stock exchange. The findings of the paper show that risk premium is time varying and indicate that stock market in South Africa is vulnerable to external shocks. Moreover, the paper finds that the time-varying GARCH-M model outperforms the fixed parameter GARCH-M model in predicting stock returns when short-term forecast horizons are used. </span></p>

With the continuous increase of China’s foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China’s stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China’s stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China’s stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China’s industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages.

One of the most important problems in modern finance is finding efficient ways to summarize and visualize the stock market data to give individuals or institutions useful information about the market behavior for investment decisions. The enormous amount of valuable data generated by the stock market has attracted researchers to explore this problem domain using different methodologies. Potential significant benefits of solving these problems motivated extensive research for years. In this paper, computational data mining methodology was used to predict seven major stock market indexes. Two learning algorithms including Linear Regression and Neural Network Standard feed-forward back prop (FFB) were tested and compared. The models were trained from four years of historical data from March 2007 to February 2011 in order to predict the major stock prices indexes in the Iran (Tehran Stock Exchange). The performance of these prediction models was evaluated using two widely used statistical metrics. We can show that using Neural Network Standard feed-forward back prop (FFB) algorithm resulted in better prediction accuracy. In addition, traditional knowledge shows that a longer training period with more training data could help to build a more accurate prediction model. However, as the stock market in Iran has been highly fluctuating in the past two years, this paper shows that data collected from a closer and shorter period could help to reduce the prediction error for such highly speculated fast changing environment.

Macroeconomic Impact on Stock Market Returns and Volatility: Evidence from Sri Lanka