Market Symmetry and Its Application to Pattern-Matching-Based Portfolio Selection

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

Mean-variance portfolio optimization is widely used by financial professionals as a fundamental strategy for constructing portfolios that achieve the highest returns for a given degree of risk tolerance. This traditional approach suffers from computational instability issues, which are often solved by integrating regularization-based methods. However, regularized mean-variance models overlook sector considerations, emphasizing a need for market-informed portfolio optimization. In this paper, we propose an extended mean-variance portfolio selection framework that incorporates a new adaptive sparse-group least absolute shrinkage and selection operator (LASSO) regularization as a penalty parameter. In the proposed model, the sparse optimal portfolio is selected considering both sectors and assets simultaneously. Moreover, the inclusion of adaptive weights in the regularization, estimated based on chosen criteria, enables the use of more market data in a Markowitz portfolio model. We also develop an efficient alternating direction method of multipliers (ADMM) algorithm that guarantees convergence, for finding the optimal sparse portfolio. The effectiveness of the suggested portfolio selection model is validated by numerical results derived from the constituents of the Standard and Poor’s 500. These results demonstrate superior performance over our selected benchmark models across various evaluation measurements.

EPMA: Efficient pattern matching algorithm for DNA sequences

Intrusion Detection System has become the important part of the computer and network security because it can effectively compensate for the lack of network security measures. However, the intrusion detection technology relies heavily on pattern matching algorithms, as the choice of pattern matching algorithms directly affect the detection rate, so it is more important. First the paper is detailed analysis the characteristics of the four kinds of single pattern matching which based on prefix search, and then it tests by the number of different pattern strings. The experimental results show that the KMP algorithm can improve effectively the speed of pattern matching. So the application of KMP algorithm can improve the efficiency of intrusion detection.

Portfolio selection is one of the most important financial and investment issues. Portfolio selection seeks to allocate a predetermined capital (wealth) over one or multiple time periods between assets and stocks in a such way that the wealth of investor (portfolio owner) is maximized the risks are minimized. In the paper, we first propose a mathematical programming model for Portfolio selection to maximize the minimum amount Sharpe ratios of portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a robust possibilistic programming model (based on necessity theory) has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model has been tested on 27 companies active in the Tehran stock market. At the end, the results of the model demonestrate the good performance of the robust possibilistic programming model.

Pattern matching is essential to a wide range of applications such as network intrusion detection, virus scanning, etc. Pattern matching algorithms normally rely on state machines to detect predefined patterns. Recently, parallel pattern matching engines, based on ASICs, FPGAs or network processors, perform matching with multiple state machines. The state migration in the matching procedure incurs intensive memory accesses. Thus, it is critical to minimize the storage of state machines such that they can be fit in on-chip or other fast memory modules to achieve high-speed pattern matching. This paper proposes novel optimization techniques, namely state re-labeling and memory partition, to reduce state machine storage. The paper also presents architectural designs based on the optimization strategy. We evaluate our design using realistic pattern sets, and the results show state machine memory reduction up to 80.1%.

The mean-variance method developed by Markowitz (1959) was aimed at obtaining optimizing portfolios. But selection of portfolio in the real world mostly deviates from this optimal criterion. In this paper we have considered this issue from an altogether different aspect and developed means for aiming at nearly optimum portfolio. We considered the risk taking propensity as the main driving force and presented a heuristic method to reach the near to the optimal state. For doing so, we have introduced the coefficient of optimism in the decision making process and simultaneously considered conditional optimum portfolio and corresponding heuristic portfolio. In the extreme situations three different human value systems can be considered as optimistic, pessimistic and risk planner. To examine the closeness between the heuristic and optimum portfolios we have carried out empirical analysis covering ten years data of fifteen companies from Nifty (2000-09). Regarding the choice of companies we have adopted random selection technique. From empirical study we have found that for moderate values of the coefficient of optimism a heuristic investor’s decision nearly coincides with the corresponding optimum portfolio. However, for extreme situations i.e. optimistic and pessimistic situations heuristic portfolio differs from optimum portfolio. Keywords: Expected return, risk, optimum portfolio, heuristic portfolio, coefficient of optimism.

A linear programming model for selection of sparse high-dimensional multiperiod portfolios

Portfolio is the set of assets either real or financial owned by investor. One asset used by investor is stock because the stock has various price along the period time. Nowadays, stock investments have been done by investors. Stock price can be either profit or loss so that it is required portfolio selection. From the various price of stock, we can compute the return, expected return, and the risk of stock. In this research, we will make the optimal portfolio which can result maximum return and minimum risk of stock with the available investation. Goal programming is the mathematical programming techniques to solve the objectives subject to some constraints where the there are priorities for each objective. An application of goal programming is portfolio selection. Portfolio selection is determining some stocks optimizing maximum return and minimum risk of stock with the available investation. The novelty of this research is Goal Programming as portfolio selection method will be optimized by Particle Swarm Optimization (PSO) so that it is called Particle Swarm Optimization-Goal Programming (PSOGP). In PSO, there is initialization of particle i.e. proportion of investation on each stock as decision variable. Particle must be constructed so that it can satisfy the constraint investation of all stocks. In optimization process, the new particle is also modified so that satisfying the constraints. In multiobjective optimization, sort the fitness on the value of the first priority objective. If first fitness have the same value of the objective, then sort them on the second priority objective, and so forth. Based on simulation, PSOGP can be applied on the portfolio selection and can optimize priority of some objectives.

Portfolio Selection with Inflation-linked Bonds and Indexation Lags

Portfolio selection with inflation-linked bonds and indexation lags

The need for efficient intrusion detection systems increases every day to protect network traffic against emerging attacks. Unfortunately, increasing network speeds and number of signatures makes it harder for the existing signature-based intrusion detection systems to keep up. This makes those systems the weak link and the bottleneck which decreases the overall network performance. Researchers found that 30%–60% of the overall processing time of signature-based intrusion detection systems is spent on pattern matching operations [1]. In this paper, we present a novel and fast software-based pattern matching algorithm to reduce the number of times to perform pattern matching. This new algorithm introduces an exclusion-inclusion filter programmed only with signatures prefixes. It filters out the clean traffic without requiring pattern matching and weeds out suspicious packets to be searched using a specially modified Wu-Manber pattern matching algorithm. The exclusion-inclusion filter is a modified Bloom filter that produces a list of probable matching signatures for each suspect packet. The remaining few suspicious packets are searched only for the probable matches. Compared to the Wu-Manber algorithm used in intrusion detection systems, the experimental results indicate a speed up of 3.4 times on average, 5.5 times for regular traffic, and 1.6 times for worst case traffic. The memory overhead added by the algorithm was limited to 0.11%.

Pattern matching with wildcards is very important in many fields such as information retrieval and bioinformatics. Suffix trees are used in pattern matching with variable length wildcards. But the construction of a suffix tree needs significant time and space overload. This paper presents a new pattern matching algorithm, PST, based on multiple suffix trees. The PST algorithm uses a cutting process to divide a string S into several parts firstly, and then establishes a suffix tree for each part of S respectively. If multiple patterns are to be retrieved, the suffix trees should be adjusted according to the cutting points: prefix sequence deletion and suffix sequence addition; prefix sequence addition and suffix sequence deletion. Theoretical analysis and experiments show that the PST algorithm can decrease the time and space overload than other peers.

Discontinuous and Pattern Matching algorithm to measure deformation having discontinuities

Journal Article A Digest and Pattern Matching-Based Intrusion Detection Engine Get access Zhongqiang Chen, Zhongqiang Chen * 1Yahoo! Inc., Santa Clara, CA 95054, USA *Corresponding author: zqchen@yahoo-inc.com Search for other works by this author on: Oxford Academic Google Scholar Yuan Zhang, Yuan Zhang 2Department of Mathematics, Florida State University, Tallahassee, FL 32306, USA Search for other works by this author on: Oxford Academic Google Scholar Zhongrong Chen, Zhongrong Chen 3ProMetrics Inc., King of Prussia, PA 19406, USA Search for other works by this author on: Oxford Academic Google Scholar Alex Delis Alex Delis 4Department of Informatics and Telecommunications, University of Athens, Athens, 15784, Greece Search for other works by this author on: Oxford Academic Google Scholar The Computer Journal, Volume 52, Issue 6, August 2009, Pages 699–723, https://doi.org/10.1093/comjnl/bxp026 Published: 15 April 2009 Article history Received: 05 October 2008 Revision received: 14 March 2009 Published: 15 April 2009