金融市场资产选择与配置策略研究
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摘要
投资者在投资过程中面临的最重要的问题是挑选证券构建组合以及进行资产配置。进行投资组合管理能够使投资者规避非系统性风险,尽量使其投资的预期收益与所承担的风险相适应。资产配置是指投资者将资金分配到不同类型资产,不同地域的市场上或者在不同板块和行业间进行资金配置。准确合理的资产配置能够降低投资风险,保证投资目标的完成。
随着我国证券市场的发展,股指期货、融资融券等金融工具的引入使得在A股市场上投资组合和策略运用将更加丰富。如何挑选股票构建组合,并且可以进一步的结合股指期货的卖空特征对冲市场风险,是投资者特别是机构投资者一直非常关注的问题。
本文主要针对上述问题展开研究。本文分为七个章节:
第一章中介绍了本文研究的背景和意义,以及目前国内外的一些相关研究成果,并且阐述了本论文的研究结构和创新点。
第二章中我们主要介绍了现代金融理论,包括投资组合管理理论和有效市场假说。以Markowitz的“均值-方差”理论、资本资产定价理论和套利定价模型构成了现代投资组合管理的理论框架,是投资组合分析的理论基础。而基于对市场有效性的判断不同,我们可以将投资策略分为主动化投资和被动化投资。
第三章中我们主要介绍了我国主要市场指数的编制方法,重点介绍了沪深300指数。另外还比较了基本面指数与其他市值加权指数的优劣,并且基于问卷调查信息和客观市场数据构建了基金投资者信心指数。
针对投资策略的不同,本文第四章提出了一个被动化投资中的指数跟踪策略和一个基于财务数据和市场交易数据的贡献度策略。指数跟踪是被动化投资中的核心问题,我们基于高维统计选元方法通过选取比较少的股票能够取得比较好的跟踪效果,并且与现有方法进行了比较分析,该策略还能够应用于股指期货的期现套利中。在第四章后半部分,分析了影响上市公司股票收益的财务数据,并且基于贡献度构建投资组合,取得了良好的实证结果。
本文第五章提出了一个基于时变系数模型的Alpha策略。该策略通过行之有效的挑选合适的个股构建具有正Alpha系数的投资组合,如果进一步结合股指期货进行操作能够实现在市场下行阶段同样保持正的收益。该方法的创新之处在于通过建立一个具有长期超额收益的指数,然后很好地追踪这个指数来构建具有超额收益的投资组合,而不是通过简单的动量或者反转策略。最后还对于投资组合的不同规模和不同的形成期、持有期进行了比较分析。
在第六章中我们提出了一个股票市场行业间资产配置的策略。该策略同样可以推广到股票、债券、货币、商品等不同的资产之间的配置。我们基于Black-Litterman框架将宏观经济变量对行业预期收益率的模型预测结果作为投资者的观点,而预测的精度(即预测方差)则反映了持有该观点的信心程度,并且进一步的用GJR-GARCH模型来刻画收益率的方差波动。这样通过模型简练而系统地捕捉到股票收益的诸多特征取代Black和Litterman最早提出的使用分析员的主观观点的主张。最后通过中国股票市场数据实证的检验了上述行业间资产配置策略。我们相信该策略能为管理投资组合风险和收益提供一个有效而持续的途径。
最后一章则回顾了全文,并且提出了文章的一些不足之处。
In the investment process, the most important things are selecting securities to construct portfolio and asset allocation. Investors can avoid the non-systematic risk and match their investment risk and expected return through portfolio management as far as possible. Asset allocation is the allocation of funds to different types of assets, or to different geographical markets or to different industries and sections in one market. Accurate and reasonable asset allocation may reduce the investment risk, and ensure the completion of investment objectives.
With the development of Chinese financial market, the portfolio strategy in stock market is being more rich and varied, especially after the stock index futures, margin trading and other financial instruments be created. How to pick stocks to construct portfolio, and moreover, with selling stock index futures to hedge the market risk and get the alpha return is one of the most important topic investors concerned.
We discussed these issues in this paper. Paper had seven sections:
Chapter 1 introduces the background and significance of this study, as well as a number of related researches at home and abroad, and describes the structure and innovation of this paper.
Chapter 2 introduces the portfolio management theory and the efficient market hypothesis. Markowitz's "Mean-Variance" theory, Capital Asset Pricing Model and Arbitrage Pricing Theory constitute the the modern portfolio management theory and is the the basis of portfolio analysis. The different judgments of the efficient of the market, we can divide the investment strategy into the active investment and passive investment.
Chapter 3 introduce the major compiling methods of market indices, espically the Shanghai and Shenzhen 300 Index. We also compare the fundamental index and market value weighted index, and construct a confidence index of fund investors based on survey information and objective data.
For different investment strategies, chapter 4 presents a passive tracking index strategy and a contribution strategy based on financial data and market transaction data. Index tracking is the core problem in a passive investment strategy. By selecting fewer stocks based on stepwise pick variance method can achieve better tracking results, and we also compared this method with existing index tracking methods. This strategy can also be applied to the arbitrage of the stock index futures. In the second half of the fourth chapter analyzes the financial data which may impact the stock returns, and construct portfolios based on contribution.
Chapter 5 presents an Alpha strategy based on the time-varying coefficient model. This strategy can construct a positive Alpha coefficient portfolio through effective selection of suitable stocks, and with selling stock index futures can get positive return in bear market. The innovation of this method is through establish a index with long-term excess returns, and then build a portfolio with excess return by tracking this index well. At the end of this chapter, we compared different size, different formation period and different holding period of the portfolio.
In chapter 6 we propose an asset allocation strategy between industries of stock market. This strategy can also be extended to allocation between different asset like stocks, bonds, currencies and commodities. Based on Black-Litterman framework, we take the predicted results of industry return which modeled through macroeconomic variables as the view of investors, and the prediction accuracy (the forecast variance) reflects the degree of confidence in holding that view, and further we use the GJR -GARCH model to describe the variance of return. Through the model to capture the stock returns characteristics concisely and systematically to replace the analyst's subjective views which Black and Litterman first proposed. Finally, we tested this asset allocation strategy among industries with empirical data of Chinese stock markets. We believe that the strategy provide an effective and sustainable way for managing risk and return portfolio.
The final chapter reviews the article and proposes some issues needing further research.
随着我国证券市场的发展,股指期货、融资融券等金融工具的引入使得在A股市场上投资组合和策略运用将更加丰富。如何挑选股票构建组合,并且可以进一步的结合股指期货的卖空特征对冲市场风险,是投资者特别是机构投资者一直非常关注的问题。
本文主要针对上述问题展开研究。本文分为七个章节:
第一章中介绍了本文研究的背景和意义,以及目前国内外的一些相关研究成果,并且阐述了本论文的研究结构和创新点。
第二章中我们主要介绍了现代金融理论,包括投资组合管理理论和有效市场假说。以Markowitz的“均值-方差”理论、资本资产定价理论和套利定价模型构成了现代投资组合管理的理论框架,是投资组合分析的理论基础。而基于对市场有效性的判断不同,我们可以将投资策略分为主动化投资和被动化投资。
第三章中我们主要介绍了我国主要市场指数的编制方法,重点介绍了沪深300指数。另外还比较了基本面指数与其他市值加权指数的优劣,并且基于问卷调查信息和客观市场数据构建了基金投资者信心指数。
针对投资策略的不同,本文第四章提出了一个被动化投资中的指数跟踪策略和一个基于财务数据和市场交易数据的贡献度策略。指数跟踪是被动化投资中的核心问题,我们基于高维统计选元方法通过选取比较少的股票能够取得比较好的跟踪效果,并且与现有方法进行了比较分析,该策略还能够应用于股指期货的期现套利中。在第四章后半部分,分析了影响上市公司股票收益的财务数据,并且基于贡献度构建投资组合,取得了良好的实证结果。
本文第五章提出了一个基于时变系数模型的Alpha策略。该策略通过行之有效的挑选合适的个股构建具有正Alpha系数的投资组合,如果进一步结合股指期货进行操作能够实现在市场下行阶段同样保持正的收益。该方法的创新之处在于通过建立一个具有长期超额收益的指数,然后很好地追踪这个指数来构建具有超额收益的投资组合,而不是通过简单的动量或者反转策略。最后还对于投资组合的不同规模和不同的形成期、持有期进行了比较分析。
在第六章中我们提出了一个股票市场行业间资产配置的策略。该策略同样可以推广到股票、债券、货币、商品等不同的资产之间的配置。我们基于Black-Litterman框架将宏观经济变量对行业预期收益率的模型预测结果作为投资者的观点,而预测的精度(即预测方差)则反映了持有该观点的信心程度,并且进一步的用GJR-GARCH模型来刻画收益率的方差波动。这样通过模型简练而系统地捕捉到股票收益的诸多特征取代Black和Litterman最早提出的使用分析员的主观观点的主张。最后通过中国股票市场数据实证的检验了上述行业间资产配置策略。我们相信该策略能为管理投资组合风险和收益提供一个有效而持续的途径。
最后一章则回顾了全文,并且提出了文章的一些不足之处。
In the investment process, the most important things are selecting securities to construct portfolio and asset allocation. Investors can avoid the non-systematic risk and match their investment risk and expected return through portfolio management as far as possible. Asset allocation is the allocation of funds to different types of assets, or to different geographical markets or to different industries and sections in one market. Accurate and reasonable asset allocation may reduce the investment risk, and ensure the completion of investment objectives.
With the development of Chinese financial market, the portfolio strategy in stock market is being more rich and varied, especially after the stock index futures, margin trading and other financial instruments be created. How to pick stocks to construct portfolio, and moreover, with selling stock index futures to hedge the market risk and get the alpha return is one of the most important topic investors concerned.
We discussed these issues in this paper. Paper had seven sections:
Chapter 1 introduces the background and significance of this study, as well as a number of related researches at home and abroad, and describes the structure and innovation of this paper.
Chapter 2 introduces the portfolio management theory and the efficient market hypothesis. Markowitz's "Mean-Variance" theory, Capital Asset Pricing Model and Arbitrage Pricing Theory constitute the the modern portfolio management theory and is the the basis of portfolio analysis. The different judgments of the efficient of the market, we can divide the investment strategy into the active investment and passive investment.
Chapter 3 introduce the major compiling methods of market indices, espically the Shanghai and Shenzhen 300 Index. We also compare the fundamental index and market value weighted index, and construct a confidence index of fund investors based on survey information and objective data.
For different investment strategies, chapter 4 presents a passive tracking index strategy and a contribution strategy based on financial data and market transaction data. Index tracking is the core problem in a passive investment strategy. By selecting fewer stocks based on stepwise pick variance method can achieve better tracking results, and we also compared this method with existing index tracking methods. This strategy can also be applied to the arbitrage of the stock index futures. In the second half of the fourth chapter analyzes the financial data which may impact the stock returns, and construct portfolios based on contribution.
Chapter 5 presents an Alpha strategy based on the time-varying coefficient model. This strategy can construct a positive Alpha coefficient portfolio through effective selection of suitable stocks, and with selling stock index futures can get positive return in bear market. The innovation of this method is through establish a index with long-term excess returns, and then build a portfolio with excess return by tracking this index well. At the end of this chapter, we compared different size, different formation period and different holding period of the portfolio.
In chapter 6 we propose an asset allocation strategy between industries of stock market. This strategy can also be extended to allocation between different asset like stocks, bonds, currencies and commodities. Based on Black-Litterman framework, we take the predicted results of industry return which modeled through macroeconomic variables as the view of investors, and the prediction accuracy (the forecast variance) reflects the degree of confidence in holding that view, and further we use the GJR -GARCH model to describe the variance of return. Through the model to capture the stock returns characteristics concisely and systematically to replace the analyst's subjective views which Black and Litterman first proposed. Finally, we tested this asset allocation strategy among industries with empirical data of Chinese stock markets. We believe that the strategy provide an effective and sustainable way for managing risk and return portfolio.
The final chapter reviews the article and proposes some issues needing further research.
引文
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