Hedge Fund vs. Non-Hedge Fund Institutional Demand and the Book-to-Market Effect

Recent studies have documented that institutional investors trade contrary to the predictions of the book-to market anomaly. We examine whether a prominent sub-group of institutional investors, namely hedge funds, differ from other institutions in terms of their trading behavior with respect to the book-to-market effect. We find that hedge funds significantly alter their trading preferences with respect to growth and value stocks, after book-to-market values become public information. More importantly, we show that hedge funds are better able to identify overpriced growth stocks compared to other institutions. Our results contribute to the literature on institutional investors’ trading with respect to stock return anomalies

Cash Flow News, Discount Rate News, and Momentum

We examine the effect of aggregate cash flow news and discount rate news on momentum returns. We find that momentum profits are higher following aggregate positive cash flow news, even in down markets or low sentiment periods. This finding expands on the evidence in Cooper et al. (2004) that momentum is significant only when past market returns are non-negative and in Antoniou et al. (2013) that momentum is weaker when sentiment is pessimistic. We find that the higher momentum profits during aggregate positive cash flow news periods are primarily driven by the losers continuing to underperform in subsequent periods. Our findings are consistent with the Hong and Stein (1999) model in the sense that gradual diffusion of contradictory news is accentuated when change in wealth is positive and relatively more permanent

Development of UCAV fleet autonomy by reinforcement learning in a wargame simulation environment

In this study, we develop a machine learning based fleet autonomy for Unmanned Combat Aerial Vehicles (UCAVs) utilizing a synthetic simulation-based wargame environment. Aircraft survivability is modeled as Markov processes. Mission success metrics are developed to introduce collision avoidance and survival probability of the fleet. Flight path planning is performed utilizing the proximal policy optimization (PPO) based reinforcement learning method to obtain attack patterns with a multi-objective mission success criteria corresponding to the mission success metrics. Performance of the proposed system is evaluated by utilizing the Monte Carlo analysis in which a wider initial position interval is used when compared to the defined interval in the training phase. This provides a preliminary insight about the generalization ability of the RL agen

Physics guided deep learning for data-driven aircraft fuel consumption modeling

This paper presents a physics-guided deep neural network framework to estimate fuel consumption of an aircraft. The framework aims to improve data-driven models’ consistency in flight regimes that are not covered by data. In particular, we guide the neural network with the equations that represent fuel flow dynamics. In addition to the empirical error, we embed this physical knowledge as several extra loss terms. Results show that our proposed model accomplishes correct predictions on the labeled test set, as well as assuring physical consistency in unseen flight regimes. The results indicate that our model, while being applicable to the aircraft’s complete flight envelope, yields lower fuel consumption error measures compared to the model-based approaches and other supervised learning techniques utilizing the same training data sets. In addition, our deep learning model produces fuel consumption trends similar to the BADA4 aircraft performance model, which is widely utilized in real-world operations, in unseen and untrained flight regimes. In contrast, the other supervised learning techniques fail to produce meaningful results. Overall, the proposed methodology enhances the explainability of data-driven models without deteriorating accuracy

Cooperative planning for an unmanned combat aerial vehicle fleet using reinforcement learning

In this study, reinforcement learning (RL)-based centralized path planning is performed for an unmanned combat aerial vehicle (UCAV) fleet in a human-made hostile environment. The proposed method provides a novel approach in which closing speed and approximate time-to-go terms are used in the reward function to obtain cooperative motion while ensuring no-fly-zones (NFZs) and time-of-arrival constraints. Proximal policy optimization (PPO) algorithm is used in the training phase of the RL agent. System performance is evaluated in two different cases. In case 1, the warfare environment contains only the target area, and simultaneous arrival is desired to obtain the saturated attack effect. In case 2, the warfare environment contains NFZs in addition to the target area and the standard saturated attack and collision avoidance requirements. Particle swarm optimization (PSO)-based cooperative path planning algorithm is implemented as the baseline method, and it is compared with the proposed algorithm in terms of execution time and developed performance metrics. Monte Carlo simulation studies are performed to evaluate the system performance. According to the simulation results, the proposed system is able to generate feasible flight paths in real-time while considering the physical and operational constraints such as acceleration limits, NFZ restrictions, simultaneous arrival, and collision avoidance requirements. In that respect, the approach provides a novel and computationally efficient method for solving the large-scale cooperative path planning for UCAV fleets

A new nonlinear lifting-line method for aerodynamic analysis and deep learning modeling of small UAVs

In this work, a computationally efficient and high-precision nonlinear aerodynamic configuration analysis method is presented for both design optimization and mathematical modeling of small unmanned aerial vehicles (UAVs). First, we have developed a novel nonlinear lifting line method which (a) provides very good match for the pre- and poststall aerodynamic behavior in comparison to experiments and computationally intensive tools, (b) generates these results in order of magnitudes less time in comparison to computationally intensive methods such as computational fluid dynamics (CFD). This method is further extended to a complete configuration analysis tool that incorporates the effects of basic fuselage geometries. Moreover, a deep learning based surrogate model is developed using data generated by the new aerodynamic tool that can characterize the nonlinear aerodynamic performance of UAVs. The major novel feature of this model is that it can predict the aerodynamic properties of UAV configurations by using only geometric parameters without the need for any special input data or pre-process phase as needed by other computational aerodynamic analysis tools. The obtained black-box function can calculate the performance of a UAV over a wide angle of attack range on the order of milliseconds, whereas CFD solutions take several days/weeks in a similar computational environment. The aerodynamic model predictions show an almost 1-1 coincidence with the numerical data even for configurations with different airfoils that are not used in model training. The developed model provides a highly capable aerodynamic solver for design optimization studies as demonstrated through an illustrative profile design example

Low Serum Triglyceride Levels as Predictors of Cardiac Death in Heart Failure Patients

Understanding the influence of sex differences on predictors of cardiac mortality rates in chronic heart failure might enable us to lengthen lifetimes and to improve lives. This study describes the influence of sex on cardiovascular mortality rates among chronic heart failure patients. From January 2003 through December 2009, we evaluated 637 consecutive patients (409 men and 228 women) with chronic heart failure, who ranged in age from 18 through 94 years (mean age, 64 ± 13 yr) and ranged in New York Heart Association (NYHA) functional class from II through IV. The mean follow-up period was 38 ± 15 months, the mean age was 64 ± 13 years, and the mean left ventricular ejection fraction was 0.27 ±0.11. By the end of the study, both sexes had similar cardiovascular mortality rates (36% men vs 37% women, P=0.559). In Cox regression analysis, NYHA functional class, triglyceride level, and history of coronary artery disease were independent predictors of cardiovascular death for women with chronic heart failure. For men with chronic heart failure, the patient\u27s age, ejection fraction, and sodium level were independent predictors of cardiovascular death. In a modern tertiary referral heart failure clinic, decreased triglyceride levels were, upon univariate analysis, predictors of poor outcomes for both men and women. However, upon Cox regression analysis, reduced triglyceride levels were independent predictors of cardiac death only in women

Controversy and consensus on the management of elevated sperm DNA fragmentation in male infertility: A global survey, current guidelines, and expert recommendations

Purpose Sperm DNA fragmentation (SDF) has been associated with male infertility and poor outcomes of assisted reproductive technology (ART). The purpose of this study was to investigate global practices related to the management of elevated SDF in infertile men, summarize the relevant professional society recommendations, and provide expert recommendations for managing this condition. Materials and Methods An online global survey on clinical practices related to SDF was disseminated to reproductive clinicians, according to the CHERRIES checklist criteria. Management protocols for various conditions associated with SDF were captured and compared to the relevant recommendations in professional society guidelines and the appropriate available evidence. Expert recommendations and consensus on the management of infertile men with elevated SDF were then formulated and adapted using the Delphi method. Results A total of 436 experts from 55 different countries submitted responses. As an initial approach, 79.1% of reproductive experts recommend lifestyle modifications for infertile men with elevated SDF, and 76.9% prescribe empiric antioxidants. Regarding antioxidant duration, 39.3% recommend 4–6 months and 38.1% recommend 3 months. For men with unexplained or idiopathic infertility, and couples experiencing recurrent miscarriages associated with elevated SDF, most respondents refer to ART 6 months after failure of conservative and empiric medical management. Infertile men with clinical varicocele, normal conventional semen parameters, and elevated SDF are offered varicocele repair immediately after diagnosis by 31.4%, and after failure of antioxidants and conservative measures by 40.9%. Sperm selection techniques and testicular sperm extraction are also management options for couples undergoing ART. For most questions, heterogenous practices were demonstrated. Conclusions This paper presents the results of a large global survey on the management of infertile men with elevated SDF and reveals a lack of consensus among clinicians. Furthermore, it demonstrates the scarcity of professional society guidelines in this regard and attempts to highlight the relevant evidence. Expert recommendations are proposed to help guide clinicians

Market Share Growth and Stock Returns

We find a negative relationship between market share growth and subsequent stock returns, three- and four-factor alphas. We report the potential explanatory role of market share growth in explaining subsequent average monthly stock returns. High (Low) market share growth firms report good (poor) operating performance and positive (negative) SUEs in the quarter in which market share growth is measured and investors overact to that good (bad) news. However, high (low) market share growth firms experience decrease (increase) in operating performance and SUEs in the subsequent quarters resulting in corrections in investors’ expectations and subsequent lower (higher) stock returns