Sharif Digital Repository

According to the rise of turnover and pace of trading, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases so, using machines to analyze big data seems critical. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. OLPS which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. This article presents an online portfolio selection algorithm. The online portfolio selection sequentially selects a portfolio over a set of assets... 

Obtaining high profit is the ultimate goal of an investor in the financial market. The key to achieve high profits in stock trading is to find the right time to trade with minimum business risk. However, it is difficult, often, to make decision about the best time to buy or sell some stocks due to the extremely dynamic and volatile behavior of the stock market. In order to resolve these problems, two steps have been followed in this research:1) Create a model to predict the final price of the stock with small error rate, and 2) Suggest the best stocks for trading to the trader. In order to achieve the goals of the first step, the stock price data of Hcltech, Maruti, Axisbank is selected and... 

By expanding the use of IT and public access to financial markets, the number of players in this area has increased and the nonlinearity of the market has become more complex. Hence, investors need specific strategies that can make profitable investment by determining the time of purchase and sale of stocks. The purpose of this research is to provide a stock trading framework for strategic portfolio management. This framework uses daily values of 18 indicators of technical analysis as features and daily trading signals as data labels for training various machine learning models, such as support vector regression, k nearest neighbors, decision tree, artificial neural network and random... 

Now a day, stock price prediction is known as one of the most challenging activities in the financial field. Research in price prediction models in financial markets, despite its many challenges, is still one of the most active areas for research. The price of non-linear financial assets is dynamic and unpredictable. Therefore, it is very difficult to arrangement and predict financial time series. Recently, many studies demonstrate that checking the news published in relation to a stock can significantly improve the accuracy of the prediction model.Among the latest techniques available for stock price prediction, we can mention deep learning models, which due to their high ability to... 

In this thesis, a hybrid method is proposed to estimate the change point in the parameters of simple linear profiles in multistage processes under monotonic changes. In monotonic changes, the type of change is not known a priori, and the only assumption is the changes are of non-decreasing (isotonic) or non-increasing (monotonic) type. In the proposed method, at first, the stages and the parameters experiencing the change are identified and then, the changes occurred in these stages and parameters are identified and examined based on the moving window approach and support vector machine (SVM) algorithm. Finally, the maximum likelihood estimator of the change point is proposed. The... 

Nowadays, capital markets play a key role in the economies of countries. Hence, this market is expanding more and more every day. In such circumstances, traditional analysis methods such as fundamental analysis and technical analysis have lost their position due to low speed and accuracy. In recent years, automated trading systems have been proposed as a solution to these problems. The online portfolio selection, which sequentially allocates capital among a set of assets aiming to maximize the final return of investment in the long run, is the core problem in algorithmic trading. In this research, we present an online portfolio selection algorithm based on pattern matching principle.... 

Algorithmic trading in financial markets with machine learning is a developing and promising field of research. Financial markets have a complex, uncertain, and dynamic nature, making them challenging for algorithmic trading. To cope with the challenges of algorithmic trading in financial markets, we propose a multi-agent deep reinforcement learning framework trained by Deep Q-learning (DQN) algorithm to perform financial trading. This framework consists of multiple cooperative agents, each of which trained on a specific timeframe, to perform financial trading on the collective intelligence of the agents. Numerical experiments are conducted on historical data of the EUR/USD currency pair.... 

Graphs are a language to describe and analyze connections and relations. Recent developments have increased graphs' applications in real-world problems such as social networks, researchers' collaborations, and chemical compounds. Now that we can extract graphs from real life, how can we model and generate graphs similar to a set of known graphs or that are very likely to exist but haven't been discovered yet? Therefore, this research will focus on the problem of graph generation. In graph generation, a set of graphs is a training dataset, and the goal of the thesis is to present an improved deep generative model to learn the training data's distribution, structure, and features.Identifying... 

It has always been a challenge for retailers to plan which of the available goods will be displayed to the customer and at what price for each. In practice, the limited storage capacity of goods, the limited capacity of shelves, or the limited capacity of displaying goods on a web page in online stores may make it more difficult to decide on the above issues. These issues have been addressed in the literature when demand for goods is clear or a good estimate of demand can be obtained based on sales data. The purpose of this study is to investigate multi period Assortment planning, pricing and inventory planning with respect to the limited capacity of storage and display of goods in a... 

Today, e-commerce systems are used by both types of users. Therefore, the systems will be exposed to systematic fraud, and fraud is one of the main sources of financial losses for organizations. Therefore, it is very important for organizations to use accurate methods to detect fraud. this field is one of the most important applications of data mining in finance. There are various challenges in fraud detection projects, and this research has divided these challenges into three categories, which are: data pre-processing due to the imbalance data set, the accuracy of the machine learning model, and uncertainty. In the first part, both oversampling and undersampling methods will be used in...