[Quantitative Course] 01_Investment and Quantitative Investment

1.1 What is investment

1.1.1 Investment in the economic sense

In social life, the term investment is often encountered and used by people, such as fixed asset investment, securities investment, education investment, health investment, and even emotional investment and so on . Some of these concepts belong to investment in the economic sense, while others just borrow the concept of "investment" in terminology. In the broad sense of economics, investment is the investment of funds or capital objects and its activity process to obtain certain expected social and economic benefits. In other words, investment includes government, financial institutions, enterprises and individuals. For the purpose of obtaining future income or benefits, all kinds of economic entities advance a certain amount of currency or objects in advance to operate a certain business.

1.1.2 Classification of investments

1. investment in kind or investment in real assets

• Physical assets, also known as real assets or tangible assets, are assets that exist in physical form, such as automobiles, houses, machinery and equipment, various raw materials, materials, etc., are fixed assets and current assets, production circulation fixed assets and non-production circulation Unification of sexual (consumable) fixed assets.
• Physical investment refers to the behavior of investors using funds for physical production, that is, for the purchase and construction of fixed assets and current assets, so as to obtain future income.

2. financial investment or financial asset investment

• Financial assets, also known as intangible assets, are assets that exist in the form of value, such as bank savings deposits, bank loans, investment funds, stocks, bonds, etc.
• Financial investment refers to the behavior of investors using funds for financial assets, that is, deposits, loans, or purchases of various securities such as stocks, bonds, and funds, in order to obtain future value-added income.

1.1.3 Financial investment

Financial investment is a commodity economy concept. It was formed on the basis of physical investment with the continuous enrichment and development of the investment concept during the development of the capitalist economy, and has gradually become more popular than physical investment. important investment behavior.

In the early stage of capitalist development, capital owners and capital users were combined. Economic entities generally directly owned production materials and capital, and engaged in production and consumption in person. Most of the investment took the form of direct investment, that is, direct investment in capital Plants, purchase equipment, purchase raw materials, engage in production and distribution activities, therefore, the early concept of investment mainly refers to physical investment.

With the development of capitalist productivity and commodity economy, the separation of capital possession and utilization of capital has increasingly become an important form of capital utilization. This is because, with the development of the commodity economy, the scale of capitalist investment continues to expand, and the capital strength of individual capitalists is increasingly difficult to meet the demand for huge capital from the ever-expanding investment scale, and there is an urgent need to raise investment funds from society beyond the scope of their own capital. , As a result, the bank credit system has developed rapidly, and the joint-stock economy has emerged as the times require. Bank credit, stock issuance, and bonds have increasingly become important sources of investment funds. Therefore, financial investment has also become an important part of the modern investment concept. Moreover, due to the increasing development and continuous improvement of the modern financial market, the importance of financial investment has become increasingly prominent. Therefore, the concept of modern investment mainly refers to financial investment. In investment works in Western academic circles, investment actually refers to financial investment, especially securities investment.

Investment is the behavior of investing resources by individuals or institutions expecting to obtain income or profits in the future. Investing can take place in many areas. For individuals, it is more of a financial investment. The risks and benefits of investment coexist, and losses such as asset impairment and time waste may occur.

• Financial field: Invest money in targets with growth potential and expect to gain benefits in the future.
• Economic field: investing capital at this stage and expecting to acquire future production capacity.
• Field of study: If you want to improve your ability in a certain field, you have to invest time, energy and even money in learning.

1.1.4 Investment varieties of individual investors

• Stock investment: including A-shares, Hong Kong stocks, US stocks, etc., is a high-risk and high-yield investment category. After an investor selects and buys a stock, if the stock price rises, the investor will benefit, and if the stock price falls, the investor will suffer a loss.
• Fund investment: mainly refers to securities investment funds. Compared with stocks, fund investment does not require stock selection, which is relatively more worry-free.
• Bond investment: including treasury bonds, financial bonds, corporate bonds, etc. Compared with stock and fund investment, bond investment has lower risks and lower returns.
• Real estate investment: In addition to self-occupation, the purchase of multiple suites is considered real estate investment. The amount of real estate investment is generally large, and if the house price rises, the profit will be considerable. However, the realization period is longer, and there is a risk of falling house prices brought about by policy regulation.

1.1.5 Investment VS Speculation

• Investing is based on fundamental analysis with a focus on long-term value. Speculation is based on news experience and cares about short-term fluctuations.
• Investment pays attention to making decisions before acting, and pursues high probability security. Speculation is dancing on the tip of a knife, pursuing rapid appreciation in the short term.

1.2 Basic process of stock investment

The stock trading process is not bad. Most of my friends should be doing A-shares. Here is an introduction

Take the Shenzhen Stock Exchange as an example. For detailed documents, please refer to:

• https://investor.szse.cn/institute/bookshelf/manualseriesbook/P020190322685818724112.pdf
• https://v.icbc.com.cn/userfiles/Resources/ICBC/haiwai/Asia/download/CN/2020/mobilebanking_cashact_cn_may2020v1.pdf

1.3 Common schools of stock investment analysis

In the stock market, investment schools are flourishing, including rigorous fundamental analysis and mysterious technical analysis, and some people even use Feng Shui to predict the market. It is entirely possible for these genres that look down on each other to make money together, and of course they may lose money together. That is to say, investment is the same as martial arts in the rivers and lakes. There is no right or wrong, only suitable or not. Before explaining our investment methods, it is necessary for us to understand some of the main investment schools in the market. Similar to Jianghu martial arts, the A-share market also has different investment schools and genres. First of all, according to the different concerns of investors, earning money from listed companies and earning money from the market (other investors) can be divided into two schools: "value school" and "market school".

1.3.1 Analysis schools of investors

1. Macro strategy analysis

Start with the general direction of macroeconomic changes, and then apply it to specific stock investment, so it is also called a top-down research method. Specifically, it is to first look at whether you should invest in stocks under the current economy, then understand the medium-term and long-term trends of the market, and understand what are the core driving factors that affect this trend, and then choose which style, theme, and industry in the context of this trend , and even which portfolio to choose.

Features: It is more about the research on the market as a whole and the "side" of the background, and then choose the most reasonable direction. This is one of the most widely used methods when professional institutions invest in stocks .

2. Value investing method

This kind of research method is also called bottom-up . Simply put, it is stock selection, and stocks with huge appreciation potential are selected. After finding a good company, hold it for a long time without paying too much attention to the short-term fluctuations in the market, and grow with the company to obtain long-term benefits. Investment gurus such as Buffett, Graham, Peter Lynch, and Fisher are all representatives of this method. However, this direct bottom-up investment method requires a deep understanding of industry development, knowing the scale of the specific industry and how big the market cake will be in the future, what are the characteristics of the industry, how the competition pattern will evolve, and the company's own What is the core competitiveness and moat, what is the business, product, profit, brand, etc., and even analyze the company's various financial data, through the data to see the real appearance of the company, and see what it will look like in the next few years.

3. Thematic event investment method

This is to judge the development trend of a certain event, and to find investment opportunities for stocks with the same attributes and characteristics by looking for exceeding expectations or manufacturing expectations. The whole can be divided into two categories : systematic themes and event themes .

• Systemic themes: Pay attention to the impact of macro factors on specific stocks. For example, in the past few years, the central bank has often released water, and there has been a theme of benefiting from interest rate cuts.
• Event theme: XX news, favorable events, corporate crisis events, etc. will be reflected in the stock price. Such as AIGC, ChatGPT shares, etc.

4. Technical Analysis

Commonly used by retail investors. Mainly take the stock price as the research object, start from the historical trend of stock price changes, look at the K-line, look at the indicators, look at the graph, and predict the changes in the future price trend. Dow theory, Jesse and Soros's psychoanalysis theory, Gann theory, etc. all belong to the technical analysis stream. There are generally three types of methods in technical analysis, one is to look at indicators, the other is to draw tangent lines, and the third is to study candlestick charts.

5. Quantitative investment method

Quantitative investment is actually quantitative investment. It is bought and sold through quantitative and computerized methods. By analyzing certain data and supported by reasonable logic, a certain strategy is used to invest and obtain income. At present, quantitative investment methods are widely used in funds. The proportion of the industry is not high, and it gradually emerged after 2014.

1.4 What is quantitative investment

1.4.1 Basic concepts of quantitative investment

Quantitative investment is not a category of financial products, but an investment trading strategy. Quantitative investment strategy is to use statistics, mathematics, information technology, artificial intelligence and other methods to replace human work to make decisions, and complete stock transactions through models to construct investment portfolios. The process of using computer technology and mathematical models to realize investment strategies.
Under normal circumstances, market research, fundamental analysis, stock selection, timing, and order placement can all be done automatically by computers. In a broad sense, it can be considered that any investment method implemented with the help of mathematical models and computers can be called quantitative investment. In the current A-share market, the more common domestic quantitative investment methods are mainly multi-factor strategies, arbitrage strategies and futures CTA strategies.
Compared with subjective investment, the biggest feature of quantitative investment strategy is that it has a complete set of trading rules based on data. In all aspects of investment decision-making, a set of completely objective quantitative standards has always been run through. For example, when the horizontal index of A stock reaches a certain threshold, a position can be opened, and how many lots to buy each time a position is opened, etc. Trading rules.

Subjective Investing VS Quantitative Investing

subjective investment	Quantitative investment
Based on the subjective judgment of the fund manager	Objective results based on model calculations
Fund managers conduct research on the macro environment, industries, and companies, and predict future trends	The core is to use computer technology to mine investment rules from massive data
Pay more attention to research depth and conduct in-depth research on a small number of stocks	Pay more attention to the breadth of research, screen targets in the whole market, and conduct multi-dimensional analysis
Concentrated holdings, slightly less stable investment	Shareholding diversification, portfolio investment
Transactions rely on subjective cognition and judgment and cannot be copied in batches	Model calculation automatically places orders, and the transaction is disciplined

1.4.2 Advantages of Quantitative Investment

The advantages of quantitative investment are manifested in the following three aspects:

1. Broader investment scope: with the help of computer technology, information collection is faster and more extensive, and the scope of analysis covers the entire market, facilitating access to more investment opportunities.

2. Programmatic trading, avoiding subjective factors: Through backtesting to confirm or falsify the effectiveness of strategies, programmatic trading automatically places orders, overcomes the weakness of human nature, and avoids the interference of subjective factors such as human emotions.

3. Data processing responds quickly to create transaction value: Automatic computer analysis is used to respond quickly, with powerful data processing and information mining capabilities, supporting high-frequency trading, and verifying the validity of the model behind each decision, making it more likely to create effective transaction value.

1.4.3 The main risks of quantitative investment

1. Strategy failure risk: The biggest risk of quantitative investment is strategy failure. But the more difficult challenge is that it is impossible to predict when the strategy will fail, and the loss of strategy failure will be very large.

2. Liquidity risk: Liquidity risk mainly refers to market financing risk, not liquidity risk in the traditional sense. It is based on the fact that the strategies of many quantitative investment funds are very similar. When many funds adopt similar strategies, once a large hedge fund needs to liquidate and sell stocks that have made profits in the past, other funds may lose money or even be forced to liquidate their positions. , which will lead to the problem of liquidity risk. Many factors in quantitative investment make it easy to homogenize, and the problem is that it will resonate, and it is more likely to generate systemic risks.

3. The risk of the model itself: Quantitative investment requires the help of a model, and the establishment of a model requires the setting of various parameters, but these parameters are difficult to estimate accurately. When the estimate is not accurate, it may cause huge losses.

1.4.4 The main reasons for the emergence of quantitative investment

1. The development of modern financial theory has made financial pricing models more scientific and accurate, such as the CAPM model and the Markowitz model. These models can quickly estimate the expected return rate of stocks and select the optimal investment portfolio.

2. The development of computer technology has made the research and implementation of quantitative investment strategies more convenient, such as using machine learning and artificial intelligence to build models and execute transactions.

3. The reduction of transaction costs also makes high-frequency trading more feasible, and at the same time allows individual investors to invest at low cost through the Internet and other channels, which promotes the popularization and development of quantitative investment.

1.5 Historical development of quantitative investment

• The historical development of quantitative investment began in the United States in the 1960s. It originated from the technology in casinos and was founded by Edward Thorpe. It later developed into a scientific stock market system and established the Princeton-Newport Fund.
• The 1970s and 1980s were the iterative period of quantitative investment technology and the development of theory. Famous quantitative fund companies include Renaissance Technology and DE Shaw.
• The 1990s was the golden period for the development of quantitative investment. Quantitative investment was applied to various investment tools in the market, and major theories developed in an all-round way. The well-known asset-capital pricing model, market efficiency theory, option pricing theory, and arbitrage theory were also produced at this time. These theories laid a scientific theoretical foundation for quantitative investment strategies to become systematic and effective strategies.
• Although quantitative investment was affected by the US financial crisis in 2008, it has since become the mainstream of the market, and is generally respected by the market because of its low risk of stable returns.

View in detail: https://www.baogaoting.com/info/90450

1.6 General process of quantitative investment

The general process of quantitative investment includes the following steps:

• Strategy Design: The idea of ​​constructing a quantitative investment strategy based on financial theory, historical data or other analytical methods.

• Backtest verification: use historical data to backtest the strategy, verify the effectiveness and feasibility of the strategy, and find ways to optimize the strategy.

• Simulation plate verification: use virtual accounts and funds to conduct simulated transactions, test the performance of strategies in the actual market, and adjust and optimize strategies.

• Real offer trading: After the previous verification and optimization, put the strategy into actual trading execution.

It should be noted that the strategic design and implementation process of quantitative investment requires rigorous, scientific and systematic methods, as well as certain technical and mathematical skills. Successful quantitative investment not only depends on the design of strategies, but also requires strict risk control and capital management.

1.7 Common Quantitative Investment Platforms

1.7.1 Platform information

platform name	data	research	Backtesting	simulated trading	Firm offer	exchange community
Poly Wide (JoinQuant)	Provide complete stock market Level1 data, financial data of listed companies, and complete suspension and restoration information from 2005 to the present. Update market data in real time and update financial data after the market. In addition, it also provides market and net value data of funds (including ETF, LOF, graded A/B funds, currency funds), financial futures data, stock index data, industry sector data, concept sector data, macro data, market data, etc.	Provides a research platform based on IPython Notebook, supports Tick-level data, and supports Python2 and Python3. Provide API (Application Programming Interface).	Supports backtesting of stocks, funds, futures and other varieties, and supports daily, minute, and tick-level backtesting.	Support daily, minute and Tick-level simulated trading of stocks, stock index futures, commodity futures, ETFs and other varieties.	Cooperate with First Capital to support automated firm trading of stocks, on-site funds, and futures.	"Jukuan Community" is very active.
Nuggets (Myquant)	Provide daily/minute/tick-level stock data for the past 10 years, as well as financial, dividend distribution, industry, sector and other data. It also provides continuous data for stock index futures and commodity futures.	Support Python, Matlab, C, C++, C# languages. Provide APIs.	Support backtesting of stocks, futures and other varieties and their mixed backtesting, and support daily, minute, and tick-level backtesting.	Support daily and minute-level simulated trading of stocks, commodity futures, stock index futures and other varieties.	Customer application and manual review are required, and manual transactions can be performed after having the firm trading authority.	The "Nuggets Quantitative Community" is highly active.
Bigquant	Provide real-time and historical data of stocks, futures, funds, etc. at the daily/minute level, as well as new types of data such as news and social networking.	Supports Python and provides AI development strategies. Provide APIs.	Supports backtesting of stocks, futures and other varieties, and supports daily, minute, and tick-level backtesting.	Supports simulated trading of stocks, futures and other varieties at the daily and minute levels.	It can push second-level trading signals, provide an API interface to connect to trading terminals, and allow users to trade manually.	"Bigquant Quantitative Community" is highly active
Rice Quant	Provide basic information on stocks, ETFs, futures (stock indexes, treasury bonds, commodity futures), and spot goods. Daily market data for stocks and ETFs over the past 10 years, and minute-line data for stocks and ETFs since 2005. Market and financial data for ETFs over the past 20+ years. Futures daily market data since 1999. Minute line data of futures since 2010. Daily and minute data of China 50 ETF, commodity options. Public opinion big data.	Provide a research platform based on IPython Notebook, supporting Python, Matlab, Excel. Provide APIs.	Supports backtesting of stocks, ETFs, futures and other varieties, and supports daily and minute-level backtesting.	Supports simulated trading of stocks, ETFs, futures and other varieties at the daily and minute levels.	Provides firm offer trading of futures.	The "Mikuang Quantitative Community" is highly active.
True Quantification	Mainly the data of financial derivatives such as commodity futures, futures options, financial futures, and stock options.	Support for strategy research using Python. Provide APIs.	It mainly provides daily, minute, and tick-level backtests for futures and options.	Using a third-party simulated trading platform, it mainly provides daily-level backtesting of futures and options.	It mainly provides firm offer trading of futures.	The "Zhenge Quantitative Community" is generally active.

1.7.2 Others

Tianqin Quantification

magic square quantization

reference

• https://www.joinquant.com/view/community/detail/7e4989804f4d3cd12532cafefeea1bcb
• https://www.econ.sdu.edu.cn/jrtzx/wljc/dl_jrtzyjrtzx.htm
• https://www.futunn.com/learn
• https://www.zhihu.com/question/38311854
• http://www.hlzq.com/main/zcgl/article.shtml?article_id=10,000,392&catalogId=2930
• https://bigquant.com/wiki/doc/fazhanshi-xiang-kexue-MIXe2BMYsA
• https://zhuanlan.zhihu.com/p/443296363
• https://zhouchenlin.github.io/%E9%87%8F%E5%8C%96%E6%8A%95%E8%B5%84%E2%80%94%E2%80%94%E7%AD%96%E7%95%A5%E4%B8%8E%E6%8A%80%E6%9C%AF.pdf
• http://www.dyhjw.com/detail/195055.html
• https://www.zhihu.com/question/276340822