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We construct an extensive data set comprising all air and high-speed rail (HSR) routes in China. We estimate that commercial air travel emits seven times the carbon emissions per passenger kilometer than HSR. Results demonstrate a strong link between air travel and air carbon emissions. Increases in China's HSR routes have contributed to significant and large negative impacts on air travel and accompanying air carbon emissions. Mode substitution from air travel to HSR has led to an 18% decline in air carbon emissions in recent years, saving the environment an annual 12 million metric tons in net carbon emissions. We determine that a $35 carbon tax could generate an additional decline of air carbon emission of 6 million tons and a net reduction of 5.3 million tons. Hence, to lower carbon emissions, policymakers can consider a carbon tax and transport policies to encourage a modal shift from air travel to HSR.
Jack Strauss; Hongchang Li; Jinli Cui. High-speed Rail's impact on airline demand and air carbon emissions in China. Transport Policy 2021, 109, 85 -97.
AMA StyleJack Strauss, Hongchang Li, Jinli Cui. High-speed Rail's impact on airline demand and air carbon emissions in China. Transport Policy. 2021; 109 ():85-97.
Chicago/Turabian StyleJack Strauss; Hongchang Li; Jinli Cui. 2021. "High-speed Rail's impact on airline demand and air carbon emissions in China." Transport Policy 109, no. : 85-97.
There is little research on the impact of air cargo networks on regional economic development, which is especially notable considering that Chinese airlines gradually adjusted their networks after the introduction of high-speed rail (HSR). This empirical study aims to fill this research gap. Firstly, we used the Ordinary Least Squares (OLS) method to study the effect of the air cargo network on the regional economy. The results show that, in eastern and central China, the higher the clustering coefficient of the domestic air cargo network, the more significant their promotion effect becomes on the GDP per capita, with cities in eastern China benefitting the most from this effect. However, for super-scale cities, the clustering coefficient of the domestic air cargo network has a significant negative effect on the GDP per capita, which is likely because both the air and HSR passenger services crowd out the development opportunities for air cargo. Secondly, we applied the Difference-in-Difference (DID) method in order to measure the impact on the regional economy caused by air cargo under the impact of HSR. The results show that the aviation network adjusted for the impact of HSR produces heterogeneous effects on cities for different regions and scales, and that the international aviation network has greater impacts on cities than the domestic network. In eastern China, HSR and air cargo (both international and domestic networks) promote economic growth simultaneously; in central China, only domestic air cargo has a positive effect on the regional economy; in western China, neither HSR nor air cargo has an obvious effect on the regional economy. Policy implications—such as encouraging the cooperation of HSR and civil aviation—are discussed, and could help bring the functions of the air cargo network in regional economic development into full play.
Lulu Hao; Na Zhang; Hongchang Li; Jack Strauss; Xuejie Liu; Xuemeng Guo. The Influence of the Air Cargo Network on the Regional Economy under the Impact of High-Speed Rail in China. Sustainability 2020, 12, 8120 .
AMA StyleLulu Hao, Na Zhang, Hongchang Li, Jack Strauss, Xuejie Liu, Xuemeng Guo. The Influence of the Air Cargo Network on the Regional Economy under the Impact of High-Speed Rail in China. Sustainability. 2020; 12 (19):8120.
Chicago/Turabian StyleLulu Hao; Na Zhang; Hongchang Li; Jack Strauss; Xuejie Liu; Xuemeng Guo. 2020. "The Influence of the Air Cargo Network on the Regional Economy under the Impact of High-Speed Rail in China." Sustainability 12, no. 19: 8120.
In this article, the authors use machine learning tools to analyze industry return predictability based on the information in lagged industry returns. Controlling for post-selection inference and multiple testing, they find significant in-sample evidence of industry return predictability. Lagged returns for the financial sector and commodity- and material-producing industries exhibit widespread predictive ability, consistent with the gradual diffusion of information across economically linked industries. Out-of-sample industry return forecasts that incorporate the information in lagged industry returns are economically valuable: Controlling for systematic risk using leading multifactor models from the literature, an industry-rotation portfolio that goes long (short) industries with the highest (lowest) forecasted returns delivers an annualized alpha of over 8%. The industry-rotation portfolio also generates substantial gains during economic downturns, including the Great Recession. TOPICS: Big data/machine learning, analysis of individual factors/risk premia, portfolio construction, performance measurement
David E. Rapach; Jack K. Strauss; Jun Tu; Guofu Zhou. Industry Return Predictability: A Machine Learning Approach. The Journal of Financial Data Science 2019, 1, 9 -28.
AMA StyleDavid E. Rapach, Jack K. Strauss, Jun Tu, Guofu Zhou. Industry Return Predictability: A Machine Learning Approach. The Journal of Financial Data Science. 2019; 1 (3):9-28.
Chicago/Turabian StyleDavid E. Rapach; Jack K. Strauss; Jun Tu; Guofu Zhou. 2019. "Industry Return Predictability: A Machine Learning Approach." The Journal of Financial Data Science 1, no. 3: 9-28.
Rapid urbanization and industrialization in Chinese cities have substantially elevated carbon emissions, and transportation plays a major role in these emissions. Due to data availability, research on the impact of both high-speed rail (HSR) and other urban transportation modes on urban carbon emissions is rare. Using a relatively large panel of 194 Chinese cities from 2008–2013, we examine the impact of HSR, conventional rail, bus, roads, and subways on urban carbon emissions. We further document the interaction of these transport modes with geo-economic variables, and more accurately measure HSR’s impact on emissions using a comprehensive accessibility metric. During this time, China developed, constructed and began to operate an extensive HSR network. Our results show that increases in HSR lead to rises in carbon emissions, emissions per GDP unit and per capita. We also find that transportation’s impact on carbon emissions differs by city size and region, and transportation modes significantly interact with GDP, population and urban area to affect carbon emissions. These interactions imply that the government’s promotion of HSR over conventional rail may have unintended consequences and boost urban carbon emissions.
Hongchang Li; Jack Strauss; Lihong Liu. A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint. Sustainability 2019, 11, 2011 .
AMA StyleHongchang Li, Jack Strauss, Lihong Liu. A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint. Sustainability. 2019; 11 (7):2011.
Chicago/Turabian StyleHongchang Li; Jack Strauss; Lihong Liu. 2019. "A Panel Investigation of High-Speed Rail (HSR) and Urban Transport on China’s Carbon Footprint." Sustainability 11, no. 7: 2011.
This paper examines the impact of High-Speed Rail (HSR) on the domestic Chinese aviation passenger market. Our dataset comprises a panel of 642 air and HSR routes from 2007 to 2014. During rapid HSR expansion from 2010 to 2014, the number of air passengers per route grew 32% for destinations that did not compete with HSR, but fell 7% for routes that faced HSR competition. A difference-in-differences approach shows that the introduction of HSR leads to more than a 50% fall in air travel over two years. Increased frequency of daily HSR routes further contributes to economically large declines in air passengers. The negative effects of HSR introduction and additional daily service to air travel increase substantially over-time and the recent expansion of HSR to Central and Western regions in China dampens demand for air travel more than coastal regions. These factors point to future challenges for aviation as China plans to expand rapidly HSR service inland.
Hongchang Li; Jack Strauss; Liu Lu. The impact of high-speed rail on civil aviation in China. Transport Policy 2018, 74, 187 -200.
AMA StyleHongchang Li, Jack Strauss, Liu Lu. The impact of high-speed rail on civil aviation in China. Transport Policy. 2018; 74 ():187-200.
Chicago/Turabian StyleHongchang Li; Jack Strauss; Liu Lu. 2018. "The impact of high-speed rail on civil aviation in China." Transport Policy 74, no. : 187-200.
Asset pricing theory predicts a positive cross‐sectional relationship between expected profitability and expected returns. However, empirical studies typically use lagged ex‐post profitability as a proxy for expected profitability. In this paper, we use out‐of‐sample combination forecasts to estimate expected industry‐level operating profit, gross profit, operating cash flow, and net income. We then construct real‐time industry‐rotation strategies based on high and low expected profitability. For each measure except gross profit, these predicted‐profitability strategies earn significant alpha with respect to the Hou et al. (2015) four‐factor model net of transaction costs and outperform strategies based on ex‐post profitability. This article is protected by copyright. All rights reserved
Andrew Detzel; Philipp Schaberl; Jack Strauss. Expected versus Ex Post Profitability in the Cross‐Section of Industry Returns. Financial Management 2018, 48, 505 -536.
AMA StyleAndrew Detzel, Philipp Schaberl, Jack Strauss. Expected versus Ex Post Profitability in the Cross‐Section of Industry Returns. Financial Management. 2018; 48 (2):505-536.
Chicago/Turabian StyleAndrew Detzel; Philipp Schaberl; Jack Strauss. 2018. "Expected versus Ex Post Profitability in the Cross‐Section of Industry Returns." Financial Management 48, no. 2: 505-536.
We document that several well-known asset-pricing implications of accruals differ for in-vestment and non-investment-related components. Exposure to an investment-accruals factor explains the cross-section of returns better than accruals themselves, and senti-ment negatively predicts this factors returns. The opposite results hold for non-investment accruals. Cash profitability only subsumes long-term non-investment accruals in the cross-section of returns and economy-wide investment accruals negatively predict stock-market returns while other accruals do not. These results challenge existing accruals-anomaly theories and resolve mixed evidence by showing the anomaly is two separate phe-nomena: a risk-based investment accruals premium and a mispricing of non-investment accruals.
Andrew Detzel; Philipp Schaberl; Jack Strauss. There are two very different accruals anomalies. European Financial Management 2018, 24, 581 -609.
AMA StyleAndrew Detzel, Philipp Schaberl, Jack Strauss. There are two very different accruals anomalies. European Financial Management. 2018; 24 (4):581-609.
Chicago/Turabian StyleAndrew Detzel; Philipp Schaberl; Jack Strauss. 2018. "There are two very different accruals anomalies." European Financial Management 24, no. 4: 581-609.
In this paper, we document that returns on Bitcoin, while largely unpredictable by macroeconomic variables, are predictable by 5- to 100-day moving averages (MAs) of its prices, both in- and out-of-sample. Simple trading strategies based on the MAs significantly outperform a buy-and-hold benchmark with a Sharpe ratio already above two. The moving average strategies generate substantial alpha and utility gains, boost annualized Sharpe ratios by 0.2 to 0.6 and significantly reduce the severity of Bitcoin drawdowns. We provide a novel equilibrium model that demonstrates, in the absence of cash flows, rational learning leads to predictability and trading with use of different MA strategies. During the recent dot.com period, the fundamentals of new tech stocks were difficult to assess. We show the same technical trading strategies also outperform the buy-and-hold benchmark in a ten-year window surrounding the NASDAQ peak.
Andrew L. Mname Detzel; Hong Mname Liu; Jack Mname Strauss; Guofu Mname Zhou; Yingzi Mname Zhu. Bitcoin: Predictability and Profitability via Technical Analysis. SSRN Electronic Journal 2018, 1 .
AMA StyleAndrew L. Mname Detzel, Hong Mname Liu, Jack Mname Strauss, Guofu Mname Zhou, Yingzi Mname Zhu. Bitcoin: Predictability and Profitability via Technical Analysis. SSRN Electronic Journal. 2018; ():1.
Chicago/Turabian StyleAndrew L. Mname Detzel; Hong Mname Liu; Jack Mname Strauss; Guofu Mname Zhou; Yingzi Mname Zhu. 2018. "Bitcoin: Predictability and Profitability via Technical Analysis." SSRN Electronic Journal , no. : 1.
Student-managed portfolios offer a practical learning environment but often miss opportunities for outperformance. The authors provide several recommendations for structuring fund trades to enhance the pedagogical experience for the students in addition to generating alpha. A strategy that targets midcap stocks offers favorable risk–return characteristics and focuses on a market capitalization category that receives relatively less attention from professional money managers. Furthermore, a formal sector allocation strategy provides an additional source of portfolio outperformance when using a metric that is robust to differences in companies across sectors. The authors document the high relative return dispersion among sectors in midcap stocks and show that enterprise value/EBITDA is a consistently effective ratio in identifying both undervalued and overvalued stocks.
J. Christopher Hughen; Jack Strauss; J.P. Tremblay. Adding Value in Student-Managed Funds: Benchmark and Sector Selection. The Journal of Trading 2017, 13, 27 -34.
AMA StyleJ. Christopher Hughen, Jack Strauss, J.P. Tremblay. Adding Value in Student-Managed Funds: Benchmark and Sector Selection. The Journal of Trading. 2017; 13 (1):27-34.
Chicago/Turabian StyleJ. Christopher Hughen; Jack Strauss; J.P. Tremblay. 2017. "Adding Value in Student-Managed Funds: Benchmark and Sector Selection." The Journal of Trading 13, no. 1: 27-34.
Student-managed portfolios offer a practical learning environment but often miss opportunities for outperformance. The authors provide several recommendations for structuring fund trades to enhance the pedagogical experience for the students in addition to generating alpha. A strategy that targets midcap stocks offers favorable risk–return characteristics and focuses on a market capitalization category that receives relatively less attention from professional money managers. Furthermore, a formal sector allocation strategy provides an additional source of portfolio outperformance when using a metric that is robust to differences in companies across sectors. The authors document the high relative return dispersion among sectors in midcap stocks and show that enterprise value/EBITDA is a consistently effective ratio in both identifying undervalued and overvalued stocks.
J. Christopher Hughen; Jack Strauss; J.P. Tremblay. Adding Value in Student-Managed Funds: Benchmark and Sector Selection. The Journal of Trading 2017, 1 .
AMA StyleJ. Christopher Hughen, Jack Strauss, J.P. Tremblay. Adding Value in Student-Managed Funds: Benchmark and Sector Selection. The Journal of Trading. 2017; ():1.
Chicago/Turabian StyleJ. Christopher Hughen; Jack Strauss; J.P. Tremblay. 2017. "Adding Value in Student-Managed Funds: Benchmark and Sector Selection." The Journal of Trading , no. : 1.
Andrew Detzel; Jack Strauss. Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios*. Review of Finance 2017, 22, 1949 -1973.
AMA StyleAndrew Detzel, Jack Strauss. Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios*. Review of Finance. 2017; 22 (5):1949-1973.
Chicago/Turabian StyleAndrew Detzel; Jack Strauss. 2017. "Combination Return Forecasts and Portfolio Allocation with the Cross-Section of Book-to-Market Ratios*." Review of Finance 22, no. 5: 1949-1973.
Our study assesses the performance of portfolios formed using out-of-sample sector forecasts and past firm fundamental ratios. Portfolio allocations based on profitability measures—gross profit, operating profit, and earnings before interest, taxes, depreciation, and amortization (EBITDA)—generate substantially better performance than the benchmark. Long/short portfolio allocations using these fundamentals possess alphas over 14% and increase Sharpe ratios by over 60%. A composite variable provides the highest payoff for firm allocations, whereas EBITDA produces the most profitable out-of-sample sector allocations. Profitability metrics are superior indicators of sustainable economic performance because these ratios are more strongly linked to future returns and cash flows than net income.
J. Christopher Hughen; Jack Strauss. Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures More Effective in Selecting Firms and Sectors? The Journal of Portfolio Management 2017, 43, 87 -101.
AMA StyleJ. Christopher Hughen, Jack Strauss. Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures More Effective in Selecting Firms and Sectors? The Journal of Portfolio Management. 2017; 43 (3):87-101.
Chicago/Turabian StyleJ. Christopher Hughen; Jack Strauss. 2017. "Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures More Effective in Selecting Firms and Sectors?" The Journal of Portfolio Management 43, no. 3: 87-101.
Motivated by the Campbell-Shiller present-value identity, we propose a new method of forecasting dividend growth that combines out-of-sample forecasts from 14 individual predictive regressions based on common return predictors. Combination forecast methods generate robust out-of-sample predictability of annual dividend growth over the entire post-war period as well as most sub-periods with out-of-sample R2 up to 18.6%. The dividend-growth forecasts coupled with the dividend-price ratio also significantly forecast annual excess returns with out-of-sample R2 up to 12.4%. In spite of robust dividend predictability, we find that most variation in the dividend-price ratio is still attributable to variation in expected returns.
Andrew L. Detzel; Jack Strauss. The Dog Has Barked for a Long Time: Dividend Growth Is Predictable. SSRN Electronic Journal 2016, 1 .
AMA StyleAndrew L. Detzel, Jack Strauss. The Dog Has Barked for a Long Time: Dividend Growth Is Predictable. SSRN Electronic Journal. 2016; ():1.
Chicago/Turabian StyleAndrew L. Detzel; Jack Strauss. 2016. "The Dog Has Barked for a Long Time: Dividend Growth Is Predictable." SSRN Electronic Journal , no. : 1.
The authors examine out-of-sample industry excess return predictability and portfolio allocation using forecasting combination methods of industry-level and aggregate accruals, book-to-market, earnings, investments, and gross profits. Out-of-sample combination forecasts generate significant industry return predictability. Substantial increases in Sharpe ratios and utility gains demonstrate that predictability is not driven primarily by higher risk. Real-time portfolio allocation strategies rotate into long positions in industries with high expected returns and short industries with low expected returns. Over the past thirty years, outof-sample combination forecasts of accounting variables have generated value-weighted industry portfolio payoffs five times greater than a buy-and-hold benchmark. The constructed portfolios consistently beat a buy-and-hold benchmark portfolio two-to-one while generating alphas that exceed 10%.
Justin Lallemand; Jack Strauss. Can We Count on Accounting Fundamentals for Industry Portfolio Allocation? The Journal of Portfolio Management 2016, 42, 70 -87.
AMA StyleJustin Lallemand, Jack Strauss. Can We Count on Accounting Fundamentals for Industry Portfolio Allocation? The Journal of Portfolio Management. 2016; 42 (4):70-87.
Chicago/Turabian StyleJustin Lallemand; Jack Strauss. 2016. "Can We Count on Accounting Fundamentals for Industry Portfolio Allocation?" The Journal of Portfolio Management 42, no. 4: 70-87.
Our study assesses the performance of portfolios formed using out-of-sample sector forecasts and past firm fundamental ratios. Portfolio allocations based on profitability measures - gross profit, operating profit, and EBITDA - generate performance substantially better than the benchmark. Long/short portfolio allocations using these fundamentals possess alphas over 14% and increase Sharpe ratios by over 60%. A composite variable provides the highest payoff for firm allocations, while EBITDA produces the most profitable out-of-sample sector allocations. Profitability metrics are superior indicators of sustainable economic performance because these ratios are more strongly linked to future returns and cash flows than net income.
John Christopher Hughen; Jack Strauss. Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures Effective in Selecting Firms and Sectors? SSRN Electronic Journal 2015, 1 .
AMA StyleJohn Christopher Hughen, Jack Strauss. Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures Effective in Selecting Firms and Sectors? SSRN Electronic Journal. 2015; ():1.
Chicago/Turabian StyleJohn Christopher Hughen; Jack Strauss. 2015. "Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures Effective in Selecting Firms and Sectors?" SSRN Electronic Journal , no. : 1.