Article Type: Other

DOI: 10.3233/AF-13019

Citation: Algorithmic Finance, vol. 2, no. 1, pp. 1-2, 2013

Authors: Sandoval Junior, Leonidas

Article Type: Research Article

Abstract: Using data from world stock exchange indices prior to and during periods of global financial crises, clusters and networks of indices are built for asset graphs based on distance thresholds and diverse periods of time, so that it is then possible to analyze how clusters are formed according to correlations among indices and how they evolve in time, particularly during times of financial crises. Further analysis is made on the eigenvectors corresponding to the second highest eigenvalues of the correlation matrices, revealing a structure peculiar to markets that operate in different time zones. We also study the survivability of connections …and of clusters through time and the influence of noise in centrality measures applied to the networks of financial indices. The results show how the world's main stock market indices evolved in the last few decades with respect to their clustering structure, how their connections survive in time, and which indices are more central, according to different criteria. In particular, we witness the early formation and evolution of two main clusters, an American and an European one, the formation of a Pacific Asian cluster, and later on, of an Arab cluster. This analysis complements previous studies of the interdependencies of stock markets worldwide. Show more

Keywords: networks, financial markets, cluster, evolution

DOI: 10.3233/AF-13015

Citation: Algorithmic Finance, vol. 2, no. 1, pp. 3-43, 2013

Authors: Huerta, Ramon | Corbacho, Fernando | Elkan, Charles

Article Type: Research Article

Abstract: This paper investigates the profitability of a trading strategy based on training a model to identify stocks with high or low predicted returns. A tail set is defined to be a group of stocks whose volatility-adjusted price change is in the highest or lowest quantile, for example the highest or lowest 5%. Each stock is represented by a set of technical and fundamental features computed using CRSP and Compustat data. A classifier is trained on historical tail sets and tested on future data. The classifier is chosen to be a nonlinear support vector machine (SVM) due to its simplicity and …effectiveness. The SVM is trained once per month, in order to adjust to changing market conditions. Portfolios are formed by ranking stocks using the classifier output. The highest ranked stocks are used for long positions and the lowest ranked ones for short sales. The Global Industry Classification Standard is used to build a model for each sector such that a total of 8 long-short portfolios for Energy, Materials, Industrials, Consumer Discretionary, Consumer Staples, Health Care, Financials, and Information Technology are formed. The data range from 1981 to 2010. Without measuring trading costs, but using 91 day holding periods to minimize these, the strategy leads to annual excess returns (Jensen alpha) of 15% with volatilities under 8% using the top 25% of the stocks of the distribution for training long positions and the bottom 25% for the short ones. Show more

Keywords: Support vector machines, sector neutral portfolios, long-short portfolios, technical analysis, fundamental analysis

DOI: 10.3233/AF-13016

Citation: Algorithmic Finance, vol. 2, no. 1, pp. 45-58, 2013

Authors: Kirilenko, Andrei | Sowers, Richard B. | Meng, Xiangqian

Article Type: Research Article

Abstract: We propose and study a stylization of high frequency trading (HFT). Our interest is an order book which consists of orders from slow liquidity traders and orders from high-frequency traders. We would like to frame a model which is amenable to the (seemingly natural) mathematical toolkit of separation of scales and which can be used to address some of the larger issues involved in HFT. The main issue to which we address our model is volatility . An important question is how volatility is affected by HFT. In our stylized model, we show how HFT increases volatility, …and can quantify this effect as a function of the parameters in our model and the separation of scales. Show more

Keywords: High frequency trading, volatility

DOI: 10.3233/AF-13017

Citation: Algorithmic Finance, vol. 2, no. 1, pp. 59-98, 2013

Authors: Bailey, David H. | López de Prado, Marcos | del Pozo, Eva

Article Type: Research Article

Abstract: The problem of capital allocation to a set of strategies could be partially avoided or at least greatly simplified with an appropriate strategy approval decision process. This paper proposes such a procedure. We begin by splitting the capital allocation problem into two sequential stages: strategy approval and portfolio optimization . Then we argue that the goal of the second stage is to beat a naïve benchmark, and the goal of the first stage is to identify which strategies improve the performance of such a naïve benchmark. We believe that this is a sensible approach, as it does not leave …all the work to the optimizer, thus adding robustness to the final outcome. We introduce the concept of the Sharpe ratio indifference curve , which represents the space of pairs (candidate strategy's Sharpe ratio, candidate strategy's correlation to the approved set) for which the Sharpe ratio of the expanded approved set remains constant. We show that selecting strategies (or portfolio managers) solely based on past Sharpe ratio will lead to suboptimal outcomes, particularly when we ignore the impact that these decisions will have on the average correlation of the portfolio. Our strategy approval theorem proves that, under certain circumstances, it is entirely possible for firms to improve their overall Sharpe ratio by hiring portfolio managers with negative expected performance. Finally, we show that these results have important practical business implications with respect to the way investment firms hire, layoff and structure payouts. Show more

Keywords: C02, G11, G14, D53, Portfolio theory, Sharpe ratio, pairwise correlation, indifference curve, diversification, free call option

DOI: 10.3233/AF-13018

Citation: Algorithmic Finance, vol. 2, no. 1, pp. 99-109, 2013