Applied Finance Letters Applied Finance Letters publishes mainly empirical research with implications and relevance for academia and finance industry. The aim is to encourage high-quality contributions that foster discussions among academics, policy makers and financial practitioners. The Journal welcomes submissions from all fields of finance, and is especially interested in innovative and original contributions. en-US <p>Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty. By publishing in Applied Finance Letters, the author(s) agree to the dissemination of their work through Applied Finance Letters.</p><p> </p><p>By publishing in Applied Finance Letters, the authors grant the Journal a Creative Commons nonexclusive worldwide license (CC-BY-ND: <span><a href="" rel="license">Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License</a>) </span>for electronic dissemination of the article via the Internet, and, a nonexclusive right to license others to reproduce, republish, transmit, and distribute the content of the journal. The authors grant the Journal the right to transfer content (without changing it), to any medium or format necessary for the purpose of preservation.</p><p> </p><p>Authors agree that the Journal will not be liable for any damages, costs, or losses whatsoever arising in any circumstances from its services, including damages arising from the breakdown of technology and difficulties with access.</p><div><span style="box-sizing: border-box; margin: 0px; padding: 0px; border: 0px; font-variant-numeric: inherit; font-stretch: inherit; line-height: inherit; font-family: 'PT Sans', Helvetica, sans-serif; font-size: 16px; vertical-align: baseline;"><br /></span></div> (Prof. Bart Frijns) (Tuwhera Publishing) Sat, 31 Dec 2016 00:00:00 +1300 OJS 60 Existence and Exploitability of Financial Analysts' Informational Leadership This paper bridges two recent studies on the role of analysts to provide new and relevant information to investors. On the one hand, the contribution of analysts to long-term price discovery on the US market is rather low. Considering earnings per share forecasts as the main output of analysts’ reports, their information share amounts to only 4.6% on average. On the other hand, trading strategies set up on these EPS forecasts are quite profitable. Self-financing portfolios yield excess returns of more than 5% over the S&amp;P 100 index for a time period of 36 years, which is persistent after controlling for the well-known risk factors. In this paper, we discuss the link between the low information shares and the high abnormal returns. We argue that information shares of analysts cannot be higher, because otherwise their forecasts would lead to excessively profitable trading strategies which are very unlikely to persist over such a long period of time. Rainer Baule, Hannes Wilke ##submission.copyrightStatement## Sat, 31 Dec 2016 00:00:00 +1300 Board Composition and Innovation <p>Corporate boards make key economic and financial decisions. Diversity in the boardroom, on hand can lead to higher innovation by increasing interaction between heterogeneous agents; on the other hand it can lead to more conflict based on the predictions of social identity theory. In an examination of US firms from 1999 to 2006, this study finds that demographic diversity; directors’ individual characteristics and affiliation are associated with higher innovation in form of patents and quality of innovation in form of citations.</p> Zenu Sharma ##submission.copyrightStatement## Sat, 31 Dec 2016 00:00:00 +1300 Multidimensional Liquidity: Evidences from Indian Stock Market <p>Various dimensions of liquidity including breadth, depth, resiliency, tightness, immediacy are examined using BSE 500 and NIFTY 500 indices from Indian Equity market. Liquidity dynamics of the stock markets were examined using trading volume, trading probability, spread, Market Efficiency coefficient, and turnover rate as they gauge different dimensions of market liquidity. We provide evidences on the order of importance of these liquidity measures in Indian stock market using machine learning tools like Artificial Neural Network (ANN) and Random Forest (RF). Findings reveal that liquidity variables collectively explains the movements of stock markets. Both these machine learning tools performs satisfactorily in terms of mean absolute percentage error. We also evidenced lower level of liquidity in Bombay Stock Exchange (BSE) than National Stock Exchange (NSE) and findings supports the liquidity enhancement program recently initiated by BSE.</p> Sharad Nath Bhattacharya, Pramit Sengupta, Mousumi Bhattacharya, Basav Roychoudhury ##submission.copyrightStatement## Sat, 31 Dec 2016 00:00:00 +1300 Moving Average Trading Rules for NASDAQ Composite Index <p>This paper tests a few moving average technical trading rules for the NASDAQ Composite and Goldman Sack commodity indexes from 1972 to 2015. Our results indicate that moving average rules do exhibit strong predictive power for NADSAQ composite index but much weaker predictive power for GSCI. Can a trader use this predictive to beat the B&amp;H strategy? We show that MA-100 days could most of the time make an abnormal profit in the case of NASDAQ composite index by considering both transaction costs and risk. </p> Chien-Ping Chen ##submission.copyrightStatement## Sat, 31 Dec 2016 00:00:00 +1300