Incomplete Information, Trading Costs and Cross-autocorrelations in Stock Returns

2004 ◽  
Vol 33 (1) ◽  
pp. 145-181 ◽  
Author(s):  
Tarun Chordia ◽  
Bhaskaran Swaminathan
Keyword(s):  
Incomplete Information ◽  
Stock Returns ◽  
Trading Costs

scholarly journals Incomplete Information and the Liquidity Premium Puzzle

2020 ◽  
Author(s):  
Yingshan Chen ◽  
Min Dai ◽  
Luis Goncalves-Pinto ◽  
Jing Xu ◽  
Cheng Yan
Keyword(s):  
Incomplete Information ◽  
Stock Returns ◽  
Empirical Support ◽  
Risk Exposure ◽  
Trading Costs ◽  
Liquidity Premium ◽  
High Turnover ◽  
Benchmark Model ◽  
Premium Puzzle ◽  
Liquidity Premia

We examine the problem of an investor who trades in a market with unobservable regime shifts. The investor learns from past prices and is subject to transaction costs. Our model generates significantly larger liquidity premia compared with a benchmark model with observable market shifts. The larger premia are driven primarily by suboptimal risk exposure, as turnover is lower under incomplete information. In contrast, the benchmark model produces (mechanically) high turnover and heavy trading costs. We provide empirical support for the amplification effect of incomplete information on the relation between trading costs and future stock returns. We also show empirically that such amplification is not driven by turnover. Overall, our results can help explain the large disconnect between theory and evidence regarding the magnitude of liquidity premia, which has been a longstanding puzzle in the literature. This paper was accepted by Kay Giesecke, finance.

scholarly journals On sparsity of eigenportfolios to reduce transaction cost

2019 ◽  
Vol 3 (1) ◽  
pp. 82-90
Author(s):  
Anqi Xiong ◽  
Ali N. Akansu
Keyword(s):  
Stock Returns ◽  
Transaction Cost ◽  
Design Methodology ◽  
Asset Returns ◽  
Statistical Characteristics ◽  
Trading Costs ◽  
Content Type ◽  
Reduce Transaction Cost ◽  
Large Size

Purpose Transaction cost becomes significant when one holds many securities in a large portfolio where capital allocations are frequently rebalanced due to variations in non-stationary statistical characteristics of the asset returns. The purpose of this paper is to employ a sparsing method to sparse the eigenportfolios, so that the transaction cost can be reduced and without any loss of its performance. Design/methodology/approach In this paper, the authors have designed pdf-optimized mid-tread Lloyd-Max quantizers based on the distribution of each eigenportfolio, and then employed them to sparse the eigenportfolios, so those small size orders may usually be ignored (sparsed), as the result, the trading costs have been reduced. Findings The authors find that the sparsing technique addressed in this paper is methodic, easy to implement for large size portfolios and it offers significant reduction in transaction cost without any loss of performance. Originality/value In this paper, the authors investigated the performance the sparsed eigenportfolios of stock returns in S&P500 Index. It is shown that the sparsing method is simple to implement and it provides high levels of sparsity without causing PNL loss. Therefore, transaction cost of managing a large size portfolio is reduced by employing such an efficient sparsity method.

scholarly journals A neural network approach for fundamental investment analysis: a case of Athens Stock Exchange

2020 ◽  
Vol 182 (3-4) ◽  
pp. 56-63
Author(s):  
Safwan Mohd Nor ◽  
◽  
Nur Haiza Muhammad Zawawi ◽  
Keyword(s):  
Stock Returns ◽  
Stock Prices ◽  
Stock Exchange ◽  
Investment Analysis ◽  
Financial Statement ◽  
Trading Costs ◽  
Network Approach ◽  
Neural Network Approach ◽  
Data Source ◽  
Athens Stock Exchange

This paper explores investment profitability in an emerging European stock market using fundamental analysis enhanced by artificial neural networks. Using a set of accounting-based financial ratios from publicly available data source, we find that these ratios possess useful information in forecasting future stock returns of Athens Stock Exchange (ATHEX) constituent firms. By combining long and short rules, the neurally reinforced fundamental strategy surpasses the unconditional buy-and-hold rule in the holdout subperiod in terms of returns (total and annualized) and risk (volatility, downside volatility and drawdown) measures. Overall results remain consistent even in the presence of trading costs. Our findings suggest that stock prices in Greece do not fully incorporate financial statement information and thus inconsistent with the principle of market efficiency at the semi-strong form.

scholarly journals An Examination of the Benefits of Factor Investing in U.K. Stock Returns

2018 ◽  
Vol 10 (4) ◽  
pp. 154
Author(s):  
Jonathan Fletcher
Keyword(s):  
Stock Returns ◽  
Bayesian Approach ◽  
Short Selling ◽  
Market Frictions ◽  
Trading Costs ◽  
Portfolio Constraints ◽  
Market Index ◽  
Factor Investing ◽  
Significant Performance ◽  
The Bayesian Approach

This study uses the Bayesian approach of Wang (1998) to examine the benefits of factor investing in U.K. stock returns in the presence of market frictions. My study finds that factor investing provides significant performance benefits when the benchmark investment universe is the market index, even in the presence of market frictions such as portfolio constraints and trading costs. However when the benchmark investment universe includes industry portfolios, market frictions, such as no short selling constraints and trading costs, tends to eliminate the benefits of factor investing. Imposing less restrictive portfolio constraints, factor investing can generate significant performance for investors with higher risk aversion levels.

Averaging of temporal memories under conditions of incomplete information

2011 ◽  
Author(s):  
Joseph Leman ◽  
Matthew S. Matell ◽  
Michael Brown
Keyword(s):  
Incomplete Information

Freedom Indices and Capital Asset Pricing Model: Some Malaysian Evidence

2020 ◽  
Vol 1 (1) ◽  
pp. 1-21
Author(s):  
Ying Tay Lee ◽  
Devinaga Rasiah ◽  
Ming Ming Lai
Keyword(s):  
Human Rights ◽  
Asset Pricing ◽  
Stock Returns ◽  
Firm Size ◽  
Explanatory Power ◽  
Least Square ◽  
Press Freedom ◽  
Capital Asset Pricing ◽  
Capital Asset ◽  
Cross Sectional

Human rights and fundamental freedoms such as economic, political, and press freedoms vary widely from country to country. It creates opportunity and risk in investment decisions. Thus, this study is carried out to examine if the explanatory power of the model for capital asset pricing could be improved when these human rights movement indices are included in the model. The sample for this study comprises of 495 stocks listed in Bursa Malaysia, covering the sampling period from 2003 to 2013. The model applied in this study employed the pooled ordinary least square regression estimation. In addition, the robustness of the model is tested by using firm size as a controlled variable. The findings show that market beta as well as the economic and press freedom indices could explain the cross-sectional stock returns of the Malaysian stock market. By controlling the firm size, it adds marginally to the explanation of the extended CAP model which incorporated economic, political, and press freedom indices.

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