Improving Wealth Management Strategies Through the Use of Reinforcement Learning Based Algorithms. A Study on the Romanian Stock Market

In the context of the growing pace of technological development and that of the transition to the knowledge-based economy, wealth management strategies have become subject to the application of new ideas. One of the fields of research that are increasing in influence in the scientific community is that of reinforcement learning-based algorithms. This trend is also manifesting in the domain of economics, where the algorithms have found a use in the field of stock trading. The use of algorithms has been tested by researchers in the last decade due to the fact that by applying these new concepts, fund managers could obtain an advantage when compared to using classic management techniques. The present paper will test the effects of applying these algorithms on the Romanian market, taking into account that it is a relatively new market, and compare it to the results obtained by applying classic optimization techniques based on passive wealth management concepts. We chose the Romanian stock market due to its recent evolution regarding the FTSE Russell ratings and the fact that the country is becoming an Eastern European hub of development in the IT sector, these facts could indicate that the Romanian stock market will become even more significant in the future at a local and maybe even at a regional level

Market Risk Management - Modeling the Distribution of Losses Using Romanian Securities

Market risk with its major components, such as the risk of interest rate instruments, currency risk, and risk related to stock and commodity investigations, represents the risk of losses in balance sheet and off-balance sheet positions, resulting from negative market price movements. Portfolios of instruments traded for short-term profits, called trading portfolios, are exposed to market risk or risk of loss, resulting from changes in the prices of instruments, such as stocks, bonds, and currencies. This paper, through theoretical and empirical methods, assesses risk by using the probability distribution of daily variations in government bond yields. Long-term government securities in most cases have a higher return due to the higher level of risk assumed regarding changes in risk factors such as interest rates, which, when raised above a certain threshold, cause a price decrease, which illustrates the price sensitivity to long-term bonds. Using Value at Risk as the main element for determining the maximum possible loss on investment in a trading book, as well as statistical tests to measure the similarity between two or more distributions such as the Kolmogorov-Smirnov test, Anderson -Darling or Chi-squared, we identified the most representative theoretical probabilistic distribution both for the value of losses and for the frequency of risk events. At the same time, the most used distributions to manage the market risk by advanced methods and, of course, the distributions used in this paper, were Weibull and Pareto (including the generalized form), as well as other distributions, because they better capture the asymmetry in queues and the presence of thick tails. Modeling the distribution of losses requires choosing from a set of probable distributions, the one with the highest log-likelihood

The Impact of Disturbances on the US Stock Market’s Spread and Investor Sentiment Through the Perspective of Risk Management

The paper aims to address a topic of interest, namely: the influence and effect of the major disruptions from recent years on one of the largest important stock markets. The purpose of the paper is to show the influence of these disruptions on the US stock market, considering market efficiency and measuring the estimated Bid-Ask spread. Using daily and weekly data sets over a period of 13 years, based on the closing stock prices of 10 companies listed in the category of the NASDAQ and NYSE stock indexes and calculating the return at (t) and (t+1) for each stock, the covariance of the two returns at (t) and (t+1) and using at t and (t+1) a "rolling window" of 21 days, which represents the trading days, as well as using the weekly data series in the same way, we obtained the relationship between the spread measurement and its size, a strong negative cross-sectional relationship, for which we performed a series of statistical tests summarized in the paper. Later, we split the data for each year separately so that we’d be able to use for each year a cross-sectional regression of the spread over the logarithmic values of the size and we noticed that there is a strong negative relationship between the two of them. According to the results obtained, it can be observed that the strongest negative correlations are in 2019 and 2021 in the case of data with daily frequency and 2020, and 2021 in the case of data with weekly frequency, for an informationally efficient market, where transaction costs are zero and in which the market price contains all the relevant information. The strongly negative correlations recorded can be explained by the fact that strong negative influences took place during these periods, which contributed to the disruption of the stock market and not only. At the same time, these negative correlations on the stock market analyzed in the last period also show a wider spread increase which theoretically shows low liquidity

RESILIENT Part 2: A Randomized, Open-Label Phase III Study of Liposomal Irinotecan Versus Topotecan in Adults With Relapsed Small Cell Lung Cancer

PURPOSE The phase III RESILIENT trial compared second-line liposomal irinotecan with topotecan in patients with small cell lung cancer (SCLC). PATIENTS AND METHODS Patients with SCLC and progression on or after first-line platinum-based chemotherapy were randomly assigned (1:1) to intravenous (IV) liposomal irinotecan (70 mg/m(2) every 2 weeks in a 6-week cycle) or IV topotecan (1.5 mg/m(2) daily for 5 consecutive days, every 3 weeks in a 6-week cycle). The primary end point was overall survival (OS). Key secondary end points included progression-free survival (PFS) and objective response rate (ORR). RESULTS Among 461 randomly assigned patients, 229 received liposomal irinotecan and 232 received topotecan. The median follow-up was 18.4 months. The median OS was 7.9 months with liposomal irinotecan versus 8.3 months with topotecan (hazard ratio [HR], 1.11 [95% CI, 0.90 to 1.37]; P = .31). The median PFS per blinded independent central review (BICR) was 4.0 months with liposomal irinotecan and 3.3 months with topotecan (HR, 0.96 [95% CI, 0.77 to 1.20]; nominal P = .71); ORR per BICR was 44.1% (95% CI, 37.6 to 50.8) and 21.6% (16.4 to 27.4), respectively. Overall, 42.0% and 83.4% of patients receiving liposomal irinotecan and topotecan, respectively, experienced grade >= 3 related treatment-emergent adverse events (TEAEs). The most common grade >= 3 related TEAEs were diarrhea (13.7%), neutropenia (8.0%), and decreased neutrophil count (4.4%) with liposomal irinotecan and neutropenia (51.6%), anemia (30.9%), and leukopenia (29.1%) with topotecan. CONCLUSION Liposomal irinotecan and topotecan demonstrated similar median OS and PFS in patients with relapsed SCLC. Although the primary end point of OS was not met, liposomal irinotecan demonstrated a higher ORR than topotecan. The safety profile of liposomal irinotecan was consistent with its known safety profile; no new safety concerns emerged