AN ANALYSIS OF THE PROFILES AND MOTIVATIONS OF HABITUAL COMMODITY SPECULATORS

The focus of this study is the habitual speculator in commodity futures markets. The speculator's activity broadens a market, creates essential liquidity, and performs an irreplaceable pricing function. Working knowledge of the profiles and motivations of habitual speculators is essential to both market theorist and policy makers. Responses to a 73 question survey were collected directly from retail commodity brokers with offices in Alabama. Each questionnaire recorded information on an individual commodity client who had traded for an extended period of time. The typical trader studied is a married, white male, age 52. He is affluent and well educated. He is a self-employed business owner who can recover from financial setbacks. He is a politically right wing conservative involved in the political process. He assumes a good deal of risk in most phases of his life. He is both an aggressive investor and an active gambler. This trader does not consider preservation of his commodity capital to be a very high trading priority. As a result, he rarely uses stop loss orders. He wins more frequently than he loses (over 51% of the time) but is an overall net loser in dollar terms. In spite of recurring trading losses, he has never made any substantial change in his basic trading style. To this trader, whether he won or lost on a particular trade is more important than the size of the win or loss. Thus he consistently cuts his profits short while letting his losses run. He also worries more about missing a move in the market by being on the sidelines than about losing by being on the wrong side of a market move; i.e., being in the action is more important than the financial consequences. Participating brokers confirmed that for the majority of the speculators studied, the primary motivation for continuous trading is the recreational utility derived largely from having a market position.Marketing,

IMM: An Imitative Reinforcement Learning Approach with Predictive Representation Learning for Automatic Market Making

Market making (MM) has attracted significant attention in financial trading owing to its essential function in ensuring market liquidity. With strong capabilities in sequential decision-making, Reinforcement Learning (RL) technology has achieved remarkable success in quantitative trading. Nonetheless, most existing RL-based MM methods focus on optimizing single-price level strategies which fail at frequent order cancellations and loss of queue priority. Strategies involving multiple price levels align better with actual trading scenarios. However, given the complexity that multi-price level strategies involves a comprehensive trading action space, the challenge of effectively training profitable RL agents for MM persists. Inspired by the efficient workflow of professional human market makers, we propose Imitative Market Maker (IMM), a novel RL framework leveraging both knowledge from suboptimal signal-based experts and direct policy interactions to develop multi-price level MM strategies efficiently. The framework start with introducing effective state and action representations adept at encoding information about multi-price level orders. Furthermore, IMM integrates a representation learning unit capable of capturing both short- and long-term market trends to mitigate adverse selection risk. Subsequently, IMM formulates an expert strategy based on signals and trains the agent through the integration of RL and imitation learning techniques, leading to efficient learning. Extensive experimental results on four real-world market datasets demonstrate that IMM outperforms current RL-based market making strategies in terms of several financial criteria. The findings of the ablation study substantiate the effectiveness of the model components

UANG PERSPEKTIF EKONOMI ISLAM

In Islamic economics, the motives that influence a person to have money are justified only for transactions and precautions. In Islam, someone having money for speculation is prohibited because money according to Islam is only a medium of exchange and a standard of value, so al-Ghazali argued that trading money with money is prohibited because it will imprison the function of money as a medium of exchange. If money can buy or be purchased with other money, then money no longer functions as a medium of exchange but as a commodity. This is prohibited in Islam. Based on his theory about the function of money as a medium of exchange, Ibn Taymiyah strongly opposed money trading because according to him this action would eliminate the function of money itself. Money in Islam is also something that has a flow concept, not a stock concept, money is always flowing, circulating in society in economic life. The conventional concept of Money Demand for Speculation (Demand for Money for Speculation). The reason for someone's demand for money based on this motive will be more in the nature of making a profit in the forex market

Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns