Mapping the trading behavior of the middle class in emerging markets: evidence from the Istanbul Stock Exchange

Predicted to grow above 4.9 billion by 2030, with an overall spending capacity of $56 trillion, the rise of the middle class in emerging markets has attracted global practitioner and academic attention. How this new wealth will be invested is a central question; yet our understanding still remains fragmented. Drawing on the literatures of international business, behavioral economics and finance and using high-frequency stock market data, we examine and map the trading behavior of the middle class in Turkey, one of the fastest rising economic powers of the East. We find that middle class traders exhibit discernible differences to professionals, with respect to risk attitudes and stock preferences (e.g. prefer lower-risk, smaller-size and ‘value’ stocks). In addition, while they typically hold small portfolios and tend to realize lower gains than professionals, their role has become considerably influential to the direction of the entire market

Revisiting the firm, industry, and country effects on profitability under recessionary and expansion periods: a multilevel analysis

Despite voluminous past research, the relevance of firm, industry, and country effects on profitability, particularly under adverse contexts, is still unclear. We reconcile institutional theory with the resource‐based view and industrial organization economics to investigate the effects of economic adversity, such as the 2008 global economic crisis. Using a three‐level random coefficient model, we examine 15,008 firms across 10 emerging and 10 developed countries for the 2005–2011 period. We find that firm effects become stronger under adversity, whereas industry effects become weaker, as well as country main and interaction effects, particularly among the emerging economies. These findings confirm our assumptions that the firm's own fate is, to a great extent, self‐determined; a reality that is even more pronounced during periods of extreme economic hardship

Forecasting the value effect of seasoned equity offering announcements

Seasoned Equity Offers (SEOs) by publicly listed firms generally result in unexpected negative share price returns, being often perceived as a signal of overvalued share prices and information asymmetries. Hence, forecasting the value effect of such announcements is of crucial importance for issuers, who wish to avoid share price dilution, but also for professional fund managers and individual investors alike. This study adopts the OR forecasting paradigm, where the latest part of the data is used as a holdout, on which a competition is performed unveiling the most effective forecasting techniques for the matter in question. We employ data from a European Market raising in total [euro]8 billion through 149 SEOs. We compare economic and econometric models to forecasting techniques mostly applied in the OR literature such as Nearest Neighbour approaches, Artificial Neural Networks as well as human Judgment. Evaluation in terms of statistical accuracy metrics indicates the superiority of the econometric models, while economic evaluation based on trading strategies and simulated profits attests expert judgement and nearest-neighbour approaches as top performers.Financial forecasting Forecasting competitions Econometric models Artificial neural networks Judgment

On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs

This article examines the predictive accuracy of simple time-series and econometric models on forecasting firm performance in terms of sales turnover. Evidence from Small and Medium sized Enterprises (SMEs) in the United Kingdom are presented. The study identifies operational rules under which the class of simple econometric regression models is more accurate than simple time-series forecasting alternatives, thus more appropriate to back-up multiple investment decisions

Beta risk and price synchronicity of bank acquirers’ common stock following merger announcements

This article demonstrates that the risk profile of acquiring banks’ common stock changes in the aftermath of a merger announcement when examining 177 large merger deals in the United States spanning from 1998 to 2010 and inclusive of the fifth and sixth merger waves. There is a tendency for beta risk to rise markedly immediately following such announcements and remains relatively high even two years afterwards. This corroborates the view that the newly consolidated big banks resulting from mergers entail higher systematic risk and, instead of providing risk diversification to shareholders, exhibit greater comovement with the market. The broad asset pricing implication here is that the ‘too big to fail’ mentality that arises from large bank mergers actually translates into more risk for shareholders and susceptibility to adverse movements in the aggregate market

Forecasting supply chain sporadic demand with nearest neighbor approaches

One of the biggest challenges in Supply Chain Management (SCM) is to forecast sporadic demand. Our forecasting methods’ arsenal includes Croston’s method, SBA and TSB as well as some more recent non-parametric advances, but none of these can identify and extrapolate patterns existing in data; this is essential as these patterns do appear quite often, driven by infrequent but nevertheless repetitive managerial practices. One could claim such patterns could be picked up by Artificial Intelligence approaches, however these do need large training datasets, unfortunately non-existent in industrial time series. Nearest Neighbors (NN) can however operate in these latter contexts, and pick up patterns even in short series. In this research we propose applying NN for supply chain data and we investigate the conditions under which these perform adequately through an extensive simulation. Furthermore, via an empirical investigation in automotive data we provide evidence that practitioners could benefit from employing supervised NN approaches. The contribution of this research is not in the development of a new theory, but in the proposition of a new conceptual framework that brings existing theory (i.e. NN) from Computer Science and Statistics and applies it successfully in an SCM setting

[Blog] How best to use a company’s resources

Using a company’s resources effectively and efficiently is, or at least should be, the focus of C-suite managers. Companies have to be cost-sustainable, value-chain efficient, and marketplace effective. What many managers fail to understand is that an emphasis on the slack resources per se rather than their configuration is fraught with pitfalls. We report the results for 3,851 large acquisitions, and the best use of a company’s resources in the M&A

Influences of family ownership on dividend policy under mandatory dividend rules

We explore the relationship between family ownership and dividend policy in an insider financial system under mandatory dividend rules. In a civil law insider institutional setting like ours, the concentration of management control in the hands of family members in combination with poor corporate governance makes the expropriation of minorities more likely for high levels of family ownership leading potentially to lower dividend payouts. We theorize on the competing effects of the alignment and entrenchment hypotheses of family control and how the dividend supply and demand mechanisms explain dividend payout decisions. We empirically demonstrate a U-shaped relationship between dividends and family ownership- akin to previously documented dividend patterns across Anglo-American firms- in line with the alignment effects on the supply of dividends and the entrenchment effects on the demand of dividends. Meanwhile, high levels of family ownership increase the likelihood that the mandatory (minimum) dividend requirement is waived. Investment opportunities and the firm’s risk profile moderate the shape and strength of the above relationships