Structural breaks in hedge fund performance and foreign exchange liquidity

Hedge fund managers are characterised as either market timers or asset pickers . Their superior performance can be attributed to either timing skill, selection ability or a combination of both. In the existing literature, average hedge fund performance across the entire time span under investigation is usually investigated and measured, and hence, potentially certain subtle but important features exhibited in different time periods can be averaged out in the analysis. This thesis investigates the structural breaks in the selection ability and timing skill of hedge fund managers. This research issue is of particular importance when the hedge fund performance before, during and after the recent financial crisis is compared and contrasted. This thesis conducts a structural break analysis of hedge fund managers performance in relation to market-wide liquidity and liquidity commonality in the foreign exchange (FX) market. Liquidity commonality captures the co-movement of individual asset liquidities. The measure adopted in the existing literature has several limitations. This thesis proposes a new measure, termed the Beta Index, which is derived from the time-varying exposure of individual liquidities to market liquidity movements. It is shown that the developed Beta Index is more able to identify the level of liquidity commonality in the FX market. It is also more flexible in measuring commonality with different data sampling frequency. The obtained empirical results have some practical implications. They show that the selection skill and timing ability of hedge fund managers are subject to regime switches. Under severe market conditions, most hedge fund managers possess the skill to time FX market-wide liquidity and are able to reduce losses from the FX market by reducing their funds FX exposure prior to the FX market-wide liquidity deteriorations. In the meantime, most hedge fund managers are able to deliver excess returns from time to time due to their selection ability. However, when sudden shocks of crisis occur, they fail to forecast the unexpected behaviour in the price of individual assets underlying the funds and display unsuccessful selection ability. In addition, the results suggest that many hedge funds are exposed to the FX liquidity commonality risk which impairs hedging strategies and diversification performance

The efficacy of bevacizumab combined with platinum-containing chemotherapy in the treatment of advanced non-small cell lung cancer in China: a systematic review and meta-analysis of randomized clinical trials

Background: Platinum-based dual-drug first-line chemotherapy is commonly employed in the treatment of patients with advanced non-small cell lung cancer (NSCLC), although its clinical efficacy is limited. Bevacizumab can antagonize vascular endothelial cell growth factor (VEGF), which inhibit tumor angiogenesis and prevent tumor invasion and development. However, a comprehensive meta-analysis evaluating the effectiveness and safety of combining bevacizumab with platinum-based chemotherapy in advanced NSCLC patients is lacking.Methods: Randomized controlled trials (RCTs) investigating the combination therapy of bevacizumab and platinum-based chemotherapy for treating advanced NSCLC were searched across six databases. Data on objective response rate (ORR), disease control rate (DCR), 1-year survival rate, 2-year survival rate, 3-year survival rate, VEGF levels, and side effects were synthesized. Relative risk degree (RR) along with 95% confidence interval (CI) was used as statistical analysis measures for binary outcomes while continuous variables were analyzed using mean difference (MD) along with 95% CI. Heterogeneity was evaluated by Chi-squared and I2 tests. If there was heterogeneity, subgroup analysis was performed. Sensitivity analysis of the main outcome measures and assessment of publication bias were also performed.Results: According to our screening criteria, a total of Forty-nine RCTs were included, involving data from 4268 patients. The results of this analysis showed that compared with platinum-containing chemotherapy alone, bevacizumab combined with platinum-containing chemotherapy significantly improved ORR (RR [95% CI], 1.53 [1.44, 1.63], p < 0.00001), DCR (RR [95% CI], 1.24 [1.19, 1.29], p < 0.0001), 1-year survival rate (RR [95% CI], 1.34 [1.15, 1.57], p = 0.0003), 2-year survival rate (RR [95% CI], 2.16 [1.35, 3.43], p = 0.001), 3-year survival rate (RR [95% CI], 2.00 [1.21, 3.30], p = 0.007). In addition, bevacizumab with platinum-containing chemotherapy observably decreased the VEGF levels (RR [95% CI], −67.35 [−91.46, −43.25], p < 0.00001).Conclusion: Combination therapy involving bevacizumab demonstrated improved antitumor effects compared to chemotherapy alone in terms of ORR, DCR, 1-year survival rate, 2-year survival rate, 3-year survival rate, and VEGF levels without an increased incidence of adverse reactions. These analyses’ results can provide clinicians guidance when selecting appropriate treatments for patients diagnosed with advanced non-small cell lung cancer

Room-temperature nonequilibrium growth of controllable ZnO nanorod arrays

In this study, controllable ZnO nanorod arrays were successfully synthesized on Si substrate at room temperature (approx. 25°C). The formation of controllable ZnO nanorod arrays has been investigated using growth media with different concentrations and molar ratios of Zn(NO3)2 to NaOH. Under such a nonequilibrium growth condition, the density and dimension of ZnO nanorod arrays were successfully adjusted through controlling the supersaturation degree, i.e., volume of growth medium. It was found that the wettability and electrowetting behaviors of ZnO nanorod arrays could be tuned through variations of nanorods density and length. Moreover, its field emission property was also optimized by changing the nanorods density and dimension

Exploration of Programmed Cell Death-Associated Characteristics and Immune infiltration in Neonatal Sepsis: New insights From Bioinformatics analysis and Machine Learning

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. to further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000)

Accurate Forecasting of Building Energy Consumption Via A Novel Ensembled Deep Learning Method Considering the Cyclic Feature

Short-term forecasting of building energy consumption (BEC) is significant for building energy reduction and real-time demand response. In this study, we propose a new method to realize half-hourly BEC prediction. In this new method, to fully utilize the existing data features and to further promote the forecasting performance, we divide the BEC data into the stable (cyclic) and stochastic components, and propose a novel hybrid model to model the stable and stochastic components respectively. The cyclic feature (CF) is extracted via the spectrum analysis, while the stochastic component is approximated by a novel Deep Belief Network (DBN) and Extreme Learning Machine (ELM) based ensembled model (DEEM). This novel hybrid model is named DEEM&nbsp;+&nbsp;CF. Furthermore, two real-world BEC experiments are performed to verify the proposed method. Also, to display the superiorities of the proposed DEEM&nbsp;+&nbsp;CF, this model is compared with the DBN, DBN&nbsp;+&nbsp;CF, ELM, ELM&nbsp;+&nbsp;CF, Support Vector Regression (SVR) and SVR&nbsp;+&nbsp;CF. Experimental results indicate that the CF has a great influence on the promotion of forecasting accuracy for approximately 20%, and DEEM&nbsp;+&nbsp;CF performance is the best among the comparative models, with at least 3%, 6%, 10% better accuracy than the DBN&nbsp;+&nbsp;CF, ELM&nbsp;+&nbsp;CF and SVR&nbsp;+&nbsp;CF respectively under the criteria of MAE. &nbsp;</p

The costs of negative affect attributable to alcohol consumption in later life: a within-between random longitudinal econometric model using UK Biobank

Aims Research demonstrates a negative relationship between alcohol use and affect, but the value of deprecation is unknown and thus cannot be included in estimates of the cost of alcohol to society. This paper aims to examine this relationship and develop econometric techniques to value the loss in affect attributable to alcohol consumption. Methods Cross-sectional (n = 129,437) and longitudinal (n = 11,352) analyses of alcohol consumers in UK Biobank data were undertaken, with depression and neuroticism as proxies of negative affect. The cross-sectional relationship between household income, negative affect and alcohol consumption were analysed using regression models, controlling for confounding variables, and using within-between random models that are robust to unobserved heterogeneity. The differential in household income required to offset alcohol’s detriment to affect was derived. Results A consistent relationship between depression and alcohol consumption (β = 0.001, z = 7.64) and neuroticism and alcohol consumption (β = 0.001, z = 9.24) was observed in cross-sectional analyses, replicated in within-between models (depression β = 0.001, z = 2.32; neuroticism β = 0.001, z = 2.33). Significant associations were found between household income and depression (cross sectional β = -0.157, z = -23.86, within-between β = -0.146, z = -9.51) and household income and neuroticism (cross sectional β = -0.166, z = -32.02, within-between β = -0.158, z = -7.44). The value of reducing alcohol consumption by one gram/day was pooled and estimated to be £209.06 (95% CI £171.84 to £246.27). Conclusions There was a robust relationship between alcohol consumption and negative affect. Econometric methods can value the intangible effects of alcohol use and may, therefore, facilitate the fiscal determination of benefit