Large Number Of Securities Research Articles

Statistical arbitrage portfolio construction based on preference relations

Statistical arbitrage methods identify mispricings in securities with the goal of building portfolios which are weakly correlated with the market. In pairs trading, an arbitrage opportunity is identified by observing relative price movements between a pair of two securities. By simultaneously observing multiple pairs, one can exploit different arbitrage opportunities and increase the performance of such methods. However, the use of a large number of pairs is difficult due to the increased probability of contradictory trade signals among different pairs. In this paper, we propose a novel portfolio construction method based on preference relation graphs, which can reconcile contradictory pairs trading signals across multiple security pairs. The proposed approach enables joint exploitation of arbitrage opportunities among a large number of securities. Experimental results using three decades of historical returns of roughly 500 stocks from the S&P 500 index show that the portfolios based on preference relations exhibit robust returns even with high transaction costs, and that their performance improves with the number of securities considered.

Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities

Abstract This article considers tests of alpha in linear factor pricing models when the number of securities, N, is much larger than the time dimension, T, of the individual return series. We focus on class of tests that are based on Student’s t-tests of individual securities which have a number of advantages over the existing standardized Wald type tests, and propose a test procedure that allows for non-Gaussianity and general forms of weakly cross-correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. We also show that the proposed test can account for some limited degree of pricing errors allowed under Ross’s arbitrage pricing theory condition. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe–Lintner capital asset pricing model and Fama–French three and five factor models are found mainly during the period of Great Recession (2007M12–2009M06).

Security and Privacy Analysis of Smartphone-Based Driver Monitoring Systems from the Developer's Point of View.

Nowadays, the whole driver monitoring system can be placed inside the vehicle driver’s smartphone, which introduces new security and privacy risks to the system. Because of the nature of the modern transportation systems, the consequences of the security issues in such systems can be crucial, leading to threat to human life and health. Moreover, despite the large number of security and privacy issues discovered in smartphone applications on a daily basis, there is no general approach for their automated analysis that can work in conditions that lack data and take into account specifics of the application area. Thus, this paper describes an original approach for a security and privacy analysis of driver monitoring systems based on smartphone sensors. This analysis uses white-box testing principles and aims to help developers evaluate and improve their products. The novelty of the proposed approach lies in combining various security and privacy analysis algorithms into a single automated approach for a specific area of application. Moreover, the suggested approach is modular and extensible, takes into account specific features of smartphone-based driver monitoring systems and works in conditions of lack or inaccessibility of data. The practical significance of the approach lies in the suggestions that are provided based on the conducted analysis. Those suggestions contain detected security and privacy issues and ways of their mitigation, together with limitations of the analysis due to the absence of data. It is assumed that such an approach would help developers take into account important aspects of security and privacy, thus reducing related issues in the developed products. An experimental evaluation of the approach is conducted on a car driver monitoring use case. In addition, the advantages and disadvantages of the proposed approach as well as future work directions are indicated.

A python-based multicriteria portfolio selection DSS

Our purpose in this article is to develop an integrated portfolio management decision support system, which takes into account the inherent multidimensional nature of the problem, while allowing the DM, i.e. investor, to incorporate his/her preferences in the decision process. The proposed DSS has been developed in Python programming language and consists of two components: The first component is associated with the security selection phase, while the second component is associated with the portfolio optimization phase. In the first phase, four discrete multicriteria methods are employed; the PROMETHEE II, the ELECTRE III, the MAUT and the TOPSIS. After the cumulative integration of the results, a series of mathematical programming models are applied in the second phase, that of multicriteria portfolio optimization; a mixed-integer quadratic programming model, a goal programming model, a genetic algorithm model, and a multiobjective PROMETHEE flow model. Finally, the proposed approach is tested through a large-scale illustrative application in several stock markets and various sectors, analyzing simultaneously a very large number of securities.

La omisión del dolo por parte del legislador en el supuesto de hecho del artículo 1233° del Código Civil

The article 1233 of the Peruvian Civil Code from 1984 developed while the repealed “Securities Act No. 16587” was still in force and regulating only four securities: The bill of exchange, promissory note, check and voucher to order. At present, have been added to the first three mentioned, a large number of securities that incorporate rights of various kinds that can fit into a promise or a payment order, that generates the need to analyze in depth the scope of the regulation. Additionally, the article analyzed, influenced by foreign law, has not provided the intentional act of the creditor, limited only to blame and not malice. This allows us to affirm the existence of unregulated behaviors with no specific legal consequence. The above described generates the need to modify this regulation and link it deeply to the “Obligations Theory” and in the new “Securities Act No. 27287

Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities

This paper proposes a novel test of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. The test is based on Student t tests of individual securities and has a number of advantages over the existing standardised Wald type tests. It allows for non-Gaussianity and general forms of weakly cross correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5;000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe-Lintner CAPM and Fama-French three factor models are found mainly during the recent financial crisis. Also we find a significant negative correlation between a twelve-months moving average p-values of the test and excess returns of long/short equity strategies (relative to the return on S&P 500) over the period November 1994 to June 2015, suggesting that abnormal profits are earned during episodes of market inefficiencies.

Testing for Alpha in Linear Factor Pricing Models with a Large Number of Securities

This paper proposes a novel test of zero pricing errors for the linear factor pricing model when the number of securities, N, can be large relative to the time dimension, T, of the return series. The test is based on Student t tests of individual securities and has a number of advantages over the existing standardised Wald type tests. It allows for non-Gaussianity and general forms of weakly cross correlated errors. It does not require estimation of an invertible error covariance matrix, it is much faster to implement, and is valid even if N is much larger than T. Monte Carlo evidence shows that the proposed test performs remarkably well even when T = 60 and N = 5;000. The test is applied to monthly returns on securities in the S&P 500 at the end of each month in real time, using rolling windows of size 60. Statistically significant evidence against Sharpe-Lintner CAPM and Fama-French three factor models are found mainly during the recent financial crisis. Also we find a significant negative correlation between a twelve-months moving average p-values of the test and excess returns of long/short equity strategies (relative to the return on S&P 500) over the period November 1994 to June 2015, suggesting that abnormal profits are earned during episodes of market inefficiencies.

Dimensions of Diversification

Within the investment industry, diversification now refers to not only the division of capital among a large number of securities but also the avoidance of risk concentration in any of a number of dimensions. Market-capitalization-weighted indexes often fail this requirement. The authors thus argue that although capitalization weighting makes a suitable benchmark, smart beta can provide a way to build indexes more suitable for investment. The authors present a methodology to measure and hence maximize diversification simultaneously across multiple dimensions. They show the practical value of this measure by using it to backtest equity portfolios. This provides an example of how the properties of assets, rather than historical returns, can be used to systematically construct well-diversified portfolios.

An examination of herding behavior in Pakistani stock market

Purpose – The purpose of this paper is to examine the investment behavior of Pakistani stock market participants, specifically with respect to their tendency to exhibit herd behavior. Design/methodology/approach – The study employed two different methodologies suggested by Christie and Huang (1995) and Chang et al. (2000) to test herd formation. Results are based on daily and monthly stock of KSE-100 index for the period 2002-2007. Findings – Results based on daily and monthly stock data from Karachi Stock Exchange indicate the non-existence of herd behavior for the period 2002-2007 and find no support for the rational asset pricing model and investor behavior found inefficient. This study denied proved evidence of herding due to market return asymmetry, high and low trading volume states and asymmetric market volatility. Macroeconomic fundamentals have insignificant role in decision-making process of investor therefore has no impact on herding behavior. However, during liquidity crisis of March 2005, Pakistani stock market exhibit herding behavior due to asymmetry of information among investors, presence of speculator and questionable badla financing-local leverage financing mechanism. Research limitations/implications – In future, this study can be improved by employing stock returns portfolios based on market capitalization or sector wise portfolio returns from KSE-100. Furthermore by identifying those factors that cause market to be overall inefficient and define the pattern of the investor trading activities. Practical implications – For an accurate valuation of assets investors should incorporate the herding factor. Social implications – As the assets are mispriced, investor behavior is uncertain and markets are inefficient, foreign investors should invest with caution, as large numbers of securities are needed to achieve the same level of diversification than in an otherwise normal market. Originality/value – In Karachi Stock Exchange, it is first attempt to uncover the herding behavior. This paper contribute to the body of knowledge by investigating the herding behavior in the emerging markets since most of the previous study concentrate more on the developed markets. Furthermore, the study also analyzed the herding behavior in different economic condition and includes economic variables and examines asymmetric effect. This study presents an integrated model to test herding behavior in Pakistani equity market. Consideration of said behavioral effect in the decision-making process of investor will make the decisions more rational and optimal.

Dynamic Asset Allocation with Predictable Returns and Transaction Costs

We propose a simple approach to dynamic multi-period portfolio choice with quadratic transaction costs. The approach is tractable in settings with a large number of securities, realistic return dynamics with multiple risk factors, many predictor variables, and stochastic volatility. We obtain a closed-form solution for a trading rule that is optimal if the problem is restricted to a broad class of strategies we dene as ‘linearity generating strategies’ (LGS). When restricted to this parametric class the highly non-linear dynamic optimization problem reduces to a deterministic linear-quadratic optimization problem in the parameters of the trading strategies. We show that the LGS approach dominates several alternative approaches in realistic settings. In particular, we demonstrate large performance dierences when there is a dynamic factor structure in returns or stochastic volatility (i.e., when the covariance matrix is stochastic), and when transaction costs covary with return volatility.

Agent based Stock Clustering for Efficient Portfolio Management

ABSTRACT This research paper proposed agent based framework for portfolio management using non-hierarchical clustering method. The framework included various agents such as data agent, clustering agent, ranking agent, portfolio manager and user agent. The data agent collected financial ratio of Nifty 50 companies from financial database. Clustering agents generated clusters and DB index computed to find optimum cluster size of each method. Validation agent evaluated the performance of k -means, k -medoids and fast k -means using intra-class inertia. Clusters generated by k -means used for investment and portfolio analysis using Markowitz model. This research helped to assemble a diversified portfolio of stocks with the use of clustering Keywords Clustering, Data mining (DM), Davis-Bouldin (DB) Index, Dunn Index, k -means, k -medoids, Partitioning Around Medoids(PAM), Silhouette index 1. INTRODUCTION Data mining is a process of automatically discovering knowledge and predicting future trends from large financial markets. It creates opportunities for companies to make proactive and knowledge-driven decision in order to gain a competitive advantage. There are varieties of DM techniques available over past decades that include classification, similarity search, cluster analysis, association rule mining. Data mining techniques are also widely applied in number of financial areas, including predicting stock prices, predicting stock indices, portfolio management, portfolio risk management, trend detection, designing recommender [27, 28]. Portfolio management is one of major problem in financial domain. In today‟s competitive financial environment, an investor wants to earn maximum profit from his assets. An investor considers an investment in securities faces with the problem of choosing from among a large number of securities. He confuses in which security he has to invest. It depends upon the risk-return characteristics of individual securities. He selects most desirable securities and likes to allocate his funds over this group of securities. Again, he faces with the problem of deciding which securities to select and how much to invest in each. The investor chooses the optimal portfolio taking into consideration the risk and return characteristics of all possible portfolios. The research work describes about an agent based framework for portfolio management using non-hierarchical clustering methods. The proposed framework consist of various agents such as data agent, clustering agent, ranking agent, user agent and portfolio manager. This framework assists investors in strategic planning and investment decision-making. This research work can help to assemble a diversified portfolio of stocks with the help of clustering and also will help investor community in specific and in turn it helps the society and economy in general for better allocation of wealth. In this research paper,

An Analytical Justification for Asset Allocation

This paper aims to provide a mathematical justification for asset allocation. Asset allocation is widely employed in practice, yet the question of its efficiency remains open. Asset allocation allows portfolio managers to concentrate on a relatively small number of assets, while security selection offers a bigger opportunity set for those who are capable of managing a large number of securities. In this paper, we study the trade-off between the ease of managing a portfolio and the size of opportunity sets depending on the number of securities under management. We derive and compare analytical expressions for the portfolio performances of asset allocation and security selection, and find that a majority of portfolio managers can actually benefit from employing asset allocation. Furthermore, the results are demonstrated empirically.

An Improved Scheme for Key Management of RFID in Vehicular Adhoc Networks

Vehicular Ad hoc Networks (VANETs) are emerging as a promising approach to improving traffic safety and providing a wide range of wireless applications for all road users. This paper addresses an improved authentication scheme for Radio frequency identification (RFID) applied in VANETs. As often being considered as a precondition for the realization of IoT, RFID can be utilized in the vehicles, so as to make the vehicles identifiable and inventoriable by computers as long as they are fitted out with radio tags. One of the public concerns is likely to focus on a certain large number of security and privacy issues. A few light symmetric key management schemes have been proposed for RFID scenarios. However, as we mentioned already, the authentication methodologies of those light symmetric key management schemes, especially in the scenarios of RFID, are still at an initial phase and call for enormous research efforts. We propose a certificate revocation status validation scheme called EKA2, using the concept of clustering from data mining to evaluate the trustiness of digital certificates.

ANN‐GA based model for stock market surveillance

Purpose – This paper attempts to develop a hybrid model using advanced data mining techniques for the detection of Stock Price Manipulation. The hybrid model detailed in this article elucidates the application of a Genetic Algorithm based Artificial Neural Network to classify stocks witnessing activities that are suggestive of potential manipulation.Design/methodology/approach – Price, volume and volatility are used as the variables for this model to capture the characteristics of stocks. An empirical analysis of this model is carried out to evaluate its ability to predict stock price manipulation in one of the largest emerging markets – India, which has a large number of securities and significant trading volumes. Further, the article compares the performance of this hybrid model with a conventional standalone model based on Quadratic Discreminant Function (QDF).Findings – Based on the results obtained, the superiority of the hybrid model over the conventional model in its ability to predict manipulation...

A Krylov subspace approach to large portfolio optimization

With a large number of securities (N) and fewer observations (T), deriving the global minimum variance portfolio requires the inversion of the singular sample covariance matrix of security returns. We introduce the Break-Down Free Generalized Minimum RESidual (BFGMRES), a Krylov subspaces method, as a fully automated approach for deriving the minimum variance portfolio. BFGMRES is a numerical algorithm that provides solutions to singular linear systems without requiring ex-ante assumptions on the covariance structure. Moreover, it is robust to illiquidity and potentially faulty data. US and international stock data are used to demonstrate the relative robustness of BFGMRES to illiquidity when compared to the “shrinkage to market” methodology developed by Ledoit and Wolf (2003). The two methods have similar performance as assessed by the Sharpe ratios and standard deviations for filtered data. In a simulation study, we show that BFGMRES is more robust than shrinkage to market in the presence of data irregularities. Indeed, when there is an illiquid stock shrinkage to market allocates almost 100% of the portfolio weights to this stock, whereas BFGMRES does not. In further simulations, we also show that when there is no illiquidity, BFGMRES exhibits superior performance than shrinkage to market when the number of stocks is high and the sample covariance matrix is highly singular.

Fast attack detection using correlation and summarizing of security alerts in grid computing networks

Due to the extensive growth of grid computing networks, security is becoming a challenge. Usual solutions are not enough to prevent sophisticated attacks fabricated by multiple users especially when the number of nodes connected to the network is changing over the time. Attackers can use multiple nodes to launch DDoS attacks which generate a large amount of security alerts. On the one hand, this large number of security alerts degrades the overall performance of the network and creates instability in the operation of the security management solutions. On the other hand, they can help in camouflaging other real attacks. To address these issues, a correlation mechanism is proposed which reduces the security alerts and continue detecting attacks in grid computing networks. To obtain the more accurate results, a major portion of the experiments are performed by launching DDoS and Brute Force (BF) attacks in real grid environment, i.e., the Grid'5000 (G5K) network.

A risk index model for portfolio selection with returns subject to experts’ estimations

Portfolio selection is concerned with selecting an optimal portfolio that can strike a balance between maximizing the return and minimizing the risk among a large number of securities. Traditionall...

A Krylov Subspace Approach to Large Portfolio Optimization

With a large number of securities (N) and fewer observations (T), deriving the global minimum variance portfolio requires the inversion of the singular sample covariance matrix of security returns. We introduce the Break-Down Free Generalized Minimum RESidual (BFGMRES), a Krylov subspaces method, as a fully automated approach for deriving the minimum variance portfolio. BFGMRES is a numerical algorithm that provides solutions to singular linear systems without requiring ex-ante assumptions on the covariance structure. Moreover, it is robust to illiquidity and potentially faulty data. US and international stock data are used to demonstrate the relative robustness of BFGMRES to illiquidity when compared to the “shrinkage to market” methodology developed by Ledoit and Wolf (2003). The two methods have similar performance as assessed by the Sharpe ratios and standard deviations for filtered data. In a simulation study, we show that BFGMRES is more robust than shrinkage to market in the presence of data irregularities. Indeed, when there is an illiquid stock shrinkage to market allocates almost 100% of the portfolio weights to this stock, whereas BFGMRES does not. In further simulations, we also show that when there is no illiquidity, BFGMRES exhibits superior performance than shrinkage to market when the number of stocks is high and the sample covariance matrix is highly singular.

Optimal mean-variance portfolio selection using Cauchy–Schwarz maximization

Fund managers highly prioritize selecting portfolios with a high Sharpe ratio. Traditionally, this task can be achieved by revising the objective function of the Markowitz mean-variance portfolio model and then resolving quadratic programming problems to obtain the maximum Sharpe ratio portfolio. This study presents a closed-form solution for the optimal Sharpe ratio portfolio by applying Cauchy–Schwarz maximization and the concept of Kuhn–Tucker conditions. An empirical example is used to demonstrate the efficiency and effectiveness of the proposed algorithms. Moreover, the proposed algorithms can also be used to obtain the optimal portfolio containing large numbers of securities, which is not possible, or at least is complicated via traditional quadratic programming approaches.

MINIMUM TRACKING ERROR PROBLEM FOR JUMP DIFFUSION STOCK-PRICE PROCESS

This paper is concerned with a problem on optimization in stock price model that allows for jumps in the stochastic processes. We show that the problem is formulated into the optimal stochastic control problem under the criterion of mean square tracking error at the end of the planning horizon. The optimal control and the optimal proportion invested in the index fund which are specified by the product of the market value of risk and the discounted terminal value of bench mark portfolio are derived by the solution to ordinary differential equations associated with an adjoint equation. 1 Introduction. The performance of investment in capital market under uncertainty is often evaluated in comparison with the benchmark portfolio. Some of the most widely known benchmark portfolios are the Standard & Poor's 500 (S & P 500), Financial Times Stock Exchange 100 Share Index, the Tokyo Stock Price Index (TOPIX) and The NIKKEI 225. However, if we actually wish to hold such a benchmark portfolio, we have to make up our minds to hold it at huge transaction, management and information collection costs, since it includes almost all or large number of securities in the market(10). Thus, it has been emphasized that the portfolio with a small number of securities which closely track the benchmark portfolio is of great concern to risk hedge and to portfolio management. Such a portfolio is called an index fund and in a case in point. So the problem can be regaraded as an optimization problem to minimize the tracking error between the benchmark portfolio and the index fund. In this paper, the tracking error is defined as the mean square error between the returns of benchmark and index fund. In a discrete time framwork, Green(3), Meada and Salkin(5) developed the relation- ship between the index fund and the frontier portfolio in the sense of the mean-variance framework. Tabata and Takeda(9) formulated this problem into a quardratic programming problem with 0-1 variables and proposed the efficient method to find an index fund which minimizes the mean square of tracking error in descrete time static model. On the other hand, much effort has been devoted to the continuous time model in the modern mathematical finance theory. As was pointed out by R. Cont and P. Tankov(1), Levy processes and other stochastic processes with jumps have become increasingly popular for modelling market fluctuations, both for risk management and option pricing purposes. Applications of the stochastic control to a financial problem, especially to the pricing of derivatives are found in Yong and Zhou(11), Kohlmann and Zhou(4), Framstad (2), Oksendal and Sulem (7) and so on. Mistui and Tabata (4) analized Levy process by means of the Teugel's martingale and showed the existence of a unique optimal hedging strategy. In this paper, we derive the optimal index fund based upon the stochastic control tech- nique. In section 2 we give our basic market model with continuous time. Section 3 deals