Labor market analysis through transformations and robust multivariate models

The work presents a robust approach to labor share analysis. The estimate of labor share presents various complexities related to the nature of the data sets to be analyzed. Typically, labor share is evaluated by using discriminant analysis and linear or generalized linear models, that do not take into account the presence of possible outliers. Moreover, the variables to be considered are often characterized by a high dimensional structure. The proposed approach has the objective of improving the estimation of the model using robust multivariate regression techniques and data transformation.     

The moderating effect of average wage and number of stores on private label market share: A hierarchical linear model analysis

Existing research on private label market share is primarily in the context of the Western market. The Chinese market context research is scarce, although private labels are developing rapidly in the past several years. This study investigates how the average wage and number of stores affect the Chinese market's private label market share. More importantly, this paper examines the moderating effect of the average wage and the number of stores on the relationship between the private label market share and product assortment as well as the relationship between the private label market share and pricing. Data collected from a Chinese supply chain dyad is analyzed to study category management using hierarchical linear models. The results reveal that the average wage and the number of stores positively affect the private label market share. Furthermore, the average wage enhances the negative effect of the number of brands, weakens the negative effect of the private label price, weakens the positive effect of national brand price. Meanwhile, the number of stores enhances the positive effect of the SKU proliferation of private label, enhances the negative effects of the number of brands, and enhances the negative effect of the private label price. This study contributes to category management. Furthermore, the findings will be valuable to domestic and international grocery marketers and retailers operating private labels in China.     

进口贸易视域下国际粮价对国内粮价的传导路径--来自玉米和大豆的证据

基于玉米和大豆等粮食国际、国内价格历史数据,运用协整及误差修正模型、脉冲响应函数等方法,揭示国际粮价对国内粮价的传导作用及影响路径。理论分析表明,国际粮价通过进口直接路径、进口产品成本路径和进口替代路径等三个子路径传导至国内粮价。实证检验发现,国际大豆价格进口直接路径和进口产品成本路径传导均比较充分,即国际大豆价格会影响大豆进口价格,进而影响国内大豆价格,最终影响国内豆油价格;国际玉米价格进口直接路径和进口产品成本路径传导则不充分,国际玉米价格会影响玉米进口价格,但是对国内玉米价格和国内玉米淀粉价格影响程度较低;而在进口替代路径中,玉米和大豆的国际价格传导均不充分。我国应继续推进以提质增效为目标的农业供给侧结构性改革,加大粮食生产科技投入力度,调动农民种粮积极性,集中力量提高粮食供给质量和效率;坚持粮食进口仅为调剂国内供求余缺的方针,把握粮食安全的主动权;建立健全粮食市场的价格调控体系及风险防控体系,规避国际粮食市场剧烈波动对国内粮食市场的影响。     

Analysis of Bitcoin prices using market and sentiment variables

This paper proposes an empirical model for analysing the dynamics of Bitcoin prices. To do this, we consider a vector error correction model over two overlapping periods: 2010–17 and 2010–19. Price discovery is achieved through the Gonzalo–Granger permanent‐transitory decomposition. The pricing factors are endogenous linear combinations of the S&P 500 index, gold price, a Google search variable associated to Bitcoin and a fear index proxied by the FED Financial Stress Index. Our empirical analysis shows that during the first period, a linear combination of four pricing factors describes the efficient Bitcoin price. The S&P 500 index and Google searches have a positive effect whereas gold prices and the fear index have a negative effect. In contrast, during the second period, the efficient price behaves idiosyncratically and can be only rationalised by individuals' search for information on the cryptocurrency. These findings provide empirical evidence on the presence of a correction in Bitcoin prices during the period 2018–19 uncorrelated to market fundamentals. We also show that standard empirical asset pricing models perform poorly for explaining Bitcoin prices.     

Trading activity and price discovery in Bitcoin futures markets

This study examines the impact of trading activities on price discovery in the Bitcoin futures markets. We find that trades of hedgers are positively correlated with the modified information shares in both CME and CBOE futures markets, suggesting that their trading promotes futures market efficiency. Retailers’ trading activity relates negatively to the price discovery of the CME Bitcoin futures and thus destabilizes the market. Speculators exert positive (negative) impact on the price discovery in the CME (CBOE) Bitcoin futures. Our finding that CME’s Bitcoin futures exhibit superior price discovery than CBOE’s provides plausible justification for CBOE’s decision in March 2019 to suspend further listings of Bitcoin futures contracts.     

Data vs. information: Using clustering techniques to enhance stock returns forecasting

This paper explores the use of clustering models of stocks to improve both (a) the prediction of stock prices and (b) the returns of trading algorithms.We cluster stocks using k-means and several alternative distance metrics, using as features quarterly financial ratios, prices and daily returns. Then, for each cluster, we train ARIMA and LSTM forecasting models to predict the daily price of each stock in the cluster. Finally, we employ the clustering-empowered forecasting models to analyze the returns of different trading algorithms.We obtain three key results: (i) LSTM models outperform ARIMA and benchmark models, obtaining positive investment returns in several scenarios; (ii) forecasting is improved by using the additional information provided by the clustering methods, therefore selecting relevant data is an important preprocessing task in the forecasting process; (iii) using information from the whole sample of stocks deteriorates the forecasting ability of LSTM models.These results have been validated using data of 240 companies of the Russell 3000 index spanning 2017 to 2022, training and testing with different subperiods.     

What determines the long-term correlation between oil prices and exchange rates?

In this study, we obtain the long-term correlation between oil prices and exchange rates by employing the dynamic conditional correlation-mixed data sampling (DCC-MIDAS) model. We then identify the factors that influence the long-term correlation using panel data analysis. We find that the long-run correlations between oil prices and exchange rates are negative for all oil-exchange rate markets except Japan. We also find that both inflation and term spread have negative effects, while the risk-free interest rate has a positive effect on the long-term correlation between oil prices and exchange rates. Importantly, the empirical results show that an increase in inflation will significantly damage the real value of the currency itself.     

Testing vertical price transmission for Vietnam's Robusta coffee

Vietnam has undergone market reforms over the last three decades; and as a consequence, the coffee sector has become increasingly market‐driven. The success of the government's liberalisation policies in terms of market efficiency is investigated by examining the transmission of both positive and negative price changes for Robusta coffee between export and farmgate prices. We used a threshold vector error correction model and high‐frequency daily data. The primary result here is that of a symmetric price transmission between export and farm‐level prices. This result holds when tested with weekly price data, derived from the daily data. Farmgate prices respond faster to decreases than increases in export prices when the long‐run deviation exceeds a certain threshold. These price changes are transmitted within several days. This research also confirms the importance of transaction costs, and other price frictions mostly ignored in prior analyses for coffee.     

On the rebound: estimating direct rebound effects for Australian households

Reducing dependence on fossil fuels by decreasing energy consumption is a common environmental policy. One mechanism used to achieve this is to encourage increased energy efficiency. However, improving efficiency may have an opposing effect and cause an increase in energy consumption if the intensity of use changes. This phenomenon is known as the rebound effect. We estimate direct rebound effects for energy use in Australia based on both aggregate residential energy use data and on household energy expenditure data. Our approach implements a new methodology developed by Hunt and Ryan (2014, Catching on the rebound: Why price elasticities are generally inappropriate measures of rebound effects. Surrey Energy Economics Discussion Paper Series SEEDS 148; 2015, Energy Economics 50, 273) that explicitly relates energy service use with energy source demand and directly incorporates measures of efficiency changes. The results indicate that the rebound effect is relatively high for energy use by Australian households. Due to the unique nature of our household data set, we can examine the influence of demographic and housing characteristics. We find that low‐income households and households with vulnerable members have the largest rebound effects. The relatively large rebound effects found here suggest that consumers gain from efficiency by improved energy services, and thus, policy targeting energy efficiency is not likely to be successful at reducing energy consumption.     

Board directors with foreign experience and stock price crash risk: Evidence from China

This study examines the impact of board directors with foreign experience (BDFEs) on stock price crash risk. We find that BDFEs help reduce crash risk. This association is robust to a series of robustness checks, including a firm fixed effects model, controlling for possibly omitted variables, and instrumental variable estimations. Moreover, we find that the negative association between BDFEs and crash risk is more pronounced for firms with more agency problems, weaker corporate governance, and less overall transparency. Our findings suggest that the characteristics of board directors matter in determining stock price crash risk.