浅析比特币的发展

随着计算机网络的不断发展,比特币成为一种流行的虚拟货币。本文通过阐述比特币的概念与特征,分析比特币的货币职能和风险,以及各国对待比特币的态度,同时提出相应的政策建议。     

美、欧央行监管比特币的做法及其对我国的启示

2015年12月5日,中国人民银行、银监会、证监会等五部委联合印发了《关于防范比特币风险的通知》,这对于保护社会公众的财产权益,保障人民币的法定货币地位,防范洗钱风险,维护金融稳定,具有重要的现实意义。美、欧央行对比特币的监管已先行一步,对我国具有一定的启示和借鉴。本文简要介绍了我国比特币交易情况及监管现状,归纳总结了美、欧央行监管比特币做法,提出我国应借鉴美、欧央行做法制定应急预案,防范比特币风险。     

Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.     

The ‘COVID’ crash of the 2020 U.S. Stock market

We employed the log-periodic power law singularity (LPPLS) methodology to systematically investigate the 2020 stock market crash in the U.S. equities sectors with different levels of total market capitalizations through four major U.S. stock market indexes, including the Wilshire 5000 Total Market index, the S&P 500 index, the S&P MidCap 400 index, and the Russell 2000 index, representing the stocks overall, the large capitalization stocks, the middle capitalization stocks and the small capitalization stocks, respectively. During the 2020 U.S. stock market crash, all four indexes lost more than a third of their values within five weeks, while both the middle capitalization stocks and the small capitalization stocks have suffered much greater losses than the large capitalization stocks and stocks overall. Our results indicate that the price trajectories of these four stock market indexes prior to the 2020 stock market crash have clearly featured the obvious LPPLS bubble pattern and were indeed in a positive bubble regime. Contrary to the popular belief that the 2020 US stock market crash was mainly due to the COVID-19 pandemic, we have shown that COVID merely served as sparks and the 2020 U.S. stock market crash had stemmed from the increasingly systemic instability of the stock market itself. We also performed the complementary post-mortem analysis of the 2020 U.S. stock market crash. Our analyses indicate that the probability density distributions of the critical time for these four indexes are positively skewed; the 2020 U.S. stock market crash originated from a bubble that had begun to form as early as September 2018; and the bubble profiles for stocks with different levels of total market capitalizations have distinct temporal patterns. This study not only sheds new light on the makings of the 2020 U.S. stock market crash but also creates a novel pipeline for future real-time crash detection and mechanism dissection of any financial market and/or economic index.     

洛阳房地产市场的泡沫危害及其对策

随着洛阳房地产市场的全面升温,关于洛阳房地产是否存在泡沫的讨论也日益受到各界关注。通过罗列和分析洛阳房地产泡沫的主要体现,文中认为目前存在泡沫,并已经膨胀,正处于警戒区。针对非合理性泡沫,结合洛阳房地产的发展展望,文中提供了相关对策。     

上市公司利润泡沫的剖析

利润是企业经营成果的集中体现,也是衡量企业经营管理业绩的主要指标之一。随着财务报告信息在市场经济中地位的不断提升,部分上市公司为了寻求对自己有利的财务成果,不惜一切手法操纵利润,甚至采用欺诈手段不合法地调整企业的利润,形成了泡沫利润,损害了投资者的利益,影响了我国证券市场的正常发展。本文针对目前上市公司财务报表的利润浮夸现象,就企业泡沫利润的形成原因、手段进行分析,并对如何识别泡沫利润的手段提出了初浅的看法,最后提出了相应的防范措施。     

Does transaction activity predict Bitcoin returns? Evidence from quantile-on-quantile analysis

This paper uses the quantile-on-quantile regression to examine the predictive power of transaction activity for Bitcoin returns over the period from January 2013 to December 2018. We measure the Bitcoin transaction activity using trading volumes, the number of unique Bitcoin transactions, and the number of unique Bitcoin addresses. Considering the onset of structural breaks, we identify considerable effects of the heterogeneity concerning the quantiles of transaction activity, which cannot be depicted fully by the traditional quantile regression method. The empirical results show that higher transaction activity tends to predict higher/lower Bitcoin returns when the market is in a bullish/bearish state. We find that the nexus is asymmetric across quantiles, depending on the sign and size of the transaction activity, and the predictive relationship intensifies in the upper or lower quantiles of the conditional distribution. In addition, this empirical evidence is in line with the volume-return association in the equity market due to private informative and noninformative trading actions. Overall, our findings suggest that transaction activity-based strategies should be made with respect to Bitcoin market performance, specifically during extreme conditions.     

Out-of-sample prediction of Bitcoin realized volatility: Do other cryptocurrencies help?

Inspired by cross-market information flows among international stock markets, we incorporate external predictive information from other cryptocurrency markets to forecast the realized volatility (RV) of Bitcoin. To make the most of such external information, we employ six widely accepted approaches to construct predictive models based on multivariate information. Our results suggest that the scaled principal component analysis (SPCA) approach steadily improves the predictive ability of the prevailing heterogeneous autoregressive (HAR) benchmark model considering both the model confidence set (MCS) test and the Diebold–Mariano (DM) test based on three widely accepted loss functions. The forecasting performance is persistent to various robustness checks and extensions. Notably, a mean–variance investor can obtain steady positive economic gains if the investment portfolio is constructed on the basis of the forecasts from the HAR-SPCA model. The results of this study show that external predictive information is statistically and economically important in forecasting Bitcoin RV.     

南宁市房地产泡沫预防与政府规制研究

目前,中国房地产业受到政府的高度重视,随着相关条例相继出台,各地政府应该根据各自地区房地产行业发展的特点采取相应对策。文章对南宁市房地产业的发展现状进行研究,根据目前存在的房价虚涨等问题加以分析,以此提出政府对治理南宁房地产的政策措施。     

Forecasting Bitcoin with technical analysis: A not-so-random forest?

This paper uses data sampled at hourly and daily frequencies to predict Bitcoin returns. We consider various advanced non-linear models based on a multitude of popular technical indicators that represent market trend, momentum, volume, and sentiment. We run a robust empirical exercise to observe the impact of forecast horizon, model type, time period, and the choice of inputs (predictors) on the forecast performance of the competing models. We find that Bitcoin prices are weakly efficient at the hourly frequency. In contrast, technical analysis combined with non-linear forecasting models becomes statistically significantly dominant relative to the random walk model on a daily horizon. Our comparative analysis identifies the random forest model as the most accurate at predicting Bitcoin. The estimated measures of the relative importance of predictors reveal that the nature of investing in the Bitcoin market evolved from trend-following to excessive momentum and sentiment in the most recent time period.     

竖向地震力的冲量计算法

一般认为地震在地表引起的破坏主要是S波的水平震动,很少考虑地震竖向力的作用。单从考虑地震水平力的作用是不易全面反映震害特征的。提出用冲量法来研究计算竖向地震力,通过对最大竖向地震力分布研究,探讨竖向地震力对建筑物的影响与破坏,为进一步提高工程抗震设计水平提供理论依据。