Risk Management in Trading: Everything that you should know

In trading, risk is inherent, as factors such as inflation, natural disasters, political instability, conflicts, etc. can impact financial markets and lead to fluctuations in the prices of financial assets (stock, bonds, etc.). Risk management in trading is a critical aspect of financial markets. Risk management usually involves strategies and techniques to mitigate potential losses and to maximise the returns.  

Effective risk management involves identifying, and assessing, followed by implementing measures to monitor and control them. Traders employ various techniques such as stop-loss orders, diversification, and position sizing to limit exposure to adverse market movements.

Additionally, risk management practices contribute to maintaining emotional stability which is a key attribute for successful trading in volatile markets. Overall, integrating robust risk management principles into trading strategies is fundamental for achieving consistent growth, and minimising the impact of unforeseen market events.

Let us find out more about one of the most important concepts in trading, that is, risk management as this blog covers:

• What is risk management in trading?
• Case studies about risk management in trading
• Key risk management techniques
• Identification of risks in trading
• Evaluation of trading risks
• How to use stop-loss and take profit in Python for risk management?
• How to avoid common trading risks in the stock market?
• Evolution of risk management in trading

What is risk management in trading?

Think about this,
When you’re about to take a trade, what's the first assessment that you do?

You’ll probably think about the future direction of the market and make trading decisions based on your anticipation. The general direction of the market is nothing but trends and in the context of risk management trends play a pivotal role. With a trend comes the risk of a trend reversal.

What if we take a long position and the upward trend reverses into a downward trend?
What if it reaches its lows?

With every assessment, there must be a risk associated. And all of these risks need to be assessed before you enter a position.⁽¹⁾

Case studies about  risk management in Trading

Now, let us see a few real-life case studies highlighting the importance of risk management in trading:⁽²⁾

Financial Crisis (2008):

Lehman Brothers' downfall in 2008 serves as a poignant reminder of the critical role risk management plays in trading. The investment bank's aggressive leveraging and exposure to risky assets left it vulnerable. Inadequate risk controls and underestimation of counterparty risks exacerbated its collapse, underscoring the need for prudent risk management practices in trading firms.

Flash Crash (2010):

On May 6, 2010, the U.S. stock market experienced a rapid and severe decline, known as the Flash Crash. Within minutes, major stock indices plummeted by nearly 10%, only to recover shortly thereafter. The event was triggered by a combination of high-frequency trading algorithms, erroneous trades, and market volatility.

COVID-19 Pandemic (2020):

The March 2020 market crash, triggered by the COVID-19 pandemic, highlighted the importance of risk management for traders.

Key risk management techniques

Risk management is the cornerstone of successful trading. This is like making sure your money stays safe while giving your trades the chance to make some gains from market ups and downs.

Here are some key techniques of risk management in trading:

Diversification

By diversifying, investors aim to minimise the impact of adverse events affecting a particular investment or sector. It helps spread risk and enhance the potential for consistent returns.

In 2019, there was a significant downturn in the automotive industry, while the information technology sector remained robust. If a trader had a diversified investment portfolio, its overall performance wouldn't have been negatively impacted by the automotive sector's decline.

Diversification involves spreading investments across different sectors or asset classes to mitigate risks associated with specific industries or market fluctuations. So, in this scenario, having investments in both the auto and IT sectors could have balanced out potential losses from one sector with gains from the other, resulting in a more stable overall performance.

You can see the example below as to how a portfolio can be diversified.

Portfolio optimisation

Portfolio optimisation is the process of constructing portfolios to maximise expected return while minimising risk. It involves analysing portfolios with different proportions of investments by calculating the risk and the return for each of the portfolios and selecting the mix of investments which achieves the desired risk versus return trade off.

Modern portfolio theory, a hypothesis which was put forth by Harry Markowitz in 1952, assumes that an investor wants to maximise a portfolio's expected return for a given amount of risk, with risk measured by the standard deviation of the portfolio’s rate of return. The risk and return of a portfolio can be plotted on a graph as:

The optimal risk portfolio is usually determined to be somewhere in the middle of the curve because as you go higher up, you take proportionately more risk for a lower return, and as you go lower in the curve the portfolio returns are very low, so investing in such a portfolio would be pointless as you can achieve similar returns by investing in risk-free assets.

The discussion table recorded a video conducted by QuantInsti exclusively for the students of the Executive Programme in Algorithmic Trading (EPAT) which you can see below.

This video talks about how position sizing, and maintaining risk management such as stop loss, and take profit are the proven tools to manage day trading risks. It explains how studying the broader market sentiment (by understanding sentiment in other asset classes like derivatives) is an important step in managing day trading risks.

Position sizing

Position sizing implies ensuring that the impact of a single trade or investment on the overall portfolio is limited. It helps control risk by defining the amount of capital at risk in any given position.

You can think of it as the opposite of going “all in”. For each trade you can limit the exposure to a certain percentage of your total capital.

For example, if you have $100 and you put all your money in one trade, you might lose all your money in a single shot. On the contrary, if you set the max exposure of 1 trade to 2% then that way you will have limited your single trade exposure to 100x2%. This would give you a chance to bet on 50 trades before you lose all your money - even if you incur a loss at every single trade.

Hedging

Hedging is an investment strategy designed to offset a potential loss. In other words, hedging is investing to reduce the risk. Hedging against market price risk means protecting yourself from adverse movements in prices by attaining a price lock. This is done by using offsetting contracts against the natural position you hold while hedging against credit risk.

Hedging can be done using derivatives, as the relationship between derivatives and their corresponding underlying is clearly defined in most cases. Other financial instruments like insurance, future contracts, swaps, options and many types of over-the-counter products are used to hedge.

Example

Let us assume company ABC produces corn flakes as a breakfast product to its consumers. Then for company ABC, fluctuations in the price of corn in the commodities market are a risk. Since company ABC is in a naturally short position (since it is selling corn as corn flakes), it will have to make sure the price of the corn does not rise invariably during the process of procurement of corn.

The company will enter into offsetting long contracts in the corn future market say at $400/bushel. Tomorrow, if the spot price of corn is $425/bushel, company ABC has successfully hedged this price variability by entering into a long futures contract for its corn procurement. And if the spot price is less than $400, say $375/bushel, still the company ABC will buy corn at $400/bushel since it has already entered into a future contract. Hence company ABC attained a price lock of $400/bushel.

Stop-loss (Exit trades)

Using stop-loss orders in risk management makes for a crucial tool in risk management, helping traders and investors protect their capital by setting predefined exit points. This ensures that losses are contained within acceptable limits.

The stop-loss orders are the automated orders that sell a security if it dips below a set price, protecting you from emotional decisions when the market throws a tantrum. Think of it as drawing a line in the sand, ensuring you retreat with most of your troops (funds) intact.

Let’s say, if the price of a stock is at $100 and you set the Stop Loss to 20%, it might not get hit until you suffer a loss of 20%. This can be problematic if the stock price drops significantly and quickly, wiping out a large portion of your capital.

On the other hand, if you set the Stop Loss to 1%, it might get triggered too often due to normal market fluctuations, preventing you from profiting from potential upward movements. This can lead to excessive trading commissions and missed opportunities.

Here are some general guidelines for setting stop-loss orders in such a case:

• Start with a percentage based on your risk tolerance.
• Consider using volatility-based stop-loss orders for risk management. These adjust automatically based on the stock's recent price movements.
• Combine stop-loss orders with other risk management techniques, such as position sizing and diversification as mentioned above.

Ultimately, the best stop-loss level is the one that you are comfortable with and that fits your overall trading strategy. It's crucial to backtest different stop-loss levels using historical data to see how they would have performed in various market conditions.

These risk management principles are often used together to create a comprehensive risk management strategy.

For example, an investor might diversify their portfolio across different asset classes and use position sizing to determine the allocation to each asset. Additionally, you can use stop-loss orders in risk management to automatically exit a position if the market moves against the investor's expectations, preventing significant losses.

Identification of risks in trading

Recognising potential risks involves understanding various market variables. Economic factors like central bank interest rate decisions or trade wars are significant.

When making trading choices, it's vital to consider these factors and their potential impact on assets.

Assessing the power of these elements to induce price fluctuations and understanding their frequency is crucial. Identifying these factors as potential threats allows for preparation, employing risk-mitigating practices such as hedging, investing in options, and diversifying assets across sectors/ industries/asset classes.

Let us now head to the evaluation of trading risks.

Suggested Reads:

• Articles on Portfolio & Risk Management
• Asset Beta and Market Beta in Python

Evaluation of trading risks

In the world of investments, understanding how your portfolio fares against the market is crucial. A key metrics, that is, beta offers valuable insights for both seasoned investors and beginners.

Think of beta as your portfolio's "volatility gauge." It tells you how much your investments fluctuate relative to the overall market.

• A beta exceeding 1 implies higher volatility, meaning your portfolio amplifies market movements (both up and down).
• Conversely, a beta below 1 suggests lower volatility, indicating relative stability and resilience in turbulent market conditions.

Now, consider you have another portfolio with a beta of 1.2.

• If the market, as represented by the chosen benchmark, goes up by 10%, your portfolio would likely increase by 12% (1.2 times the market movement).
• On the flip side, if the market drops by 10%, your portfolio might experience a 12% decline. The beta exceeding 1 signals that your portfolio is more volatile than the market – it magnifies both gains and losses.
• Conversely, a portfolio with a beta of 0.8 would be expected to show less volatility, moving only 80% with the market. This lower beta suggests a more stable and conservative investment approach.

Why is beta important?

• Understanding beta helps assess your portfolio's risk profile.
• High-beta investments offer the potential for amplified returns but also expose you to greater volatility.
• Low-beta investments provide stability but may offer lower returns.

How to use stop-loss and take profit in Python for risk management?

Here are the steps to utilise fixed stop-loss and take profit in Python for risk management:

• Step 1 - Import libraries and read the data
• Step 2 - Initialise the Variables
• Step 3 - Run the Backtest
• Step 4 - Plot the Entry and Exits
• Step 5 - Plot PnL

Step 1 - Import libraries and read the data

Output:

Step 2 - Initialise the Variables

Let's initialise some more variables to store the stop-loss price and take-profit price, as well as the stop-loss percentage and take-profit percentage.

We will use stop-loss percentage and take-profit percentage to compute stop-loss price and take-profit price.

In case you are wondering what a trade book is; a trade book is nothing but an instrument that keeps a track of all the historical trades. It stores important information such as the price and time of buying the stock, as well as the price and time of selling the stock.

Now you will create a Dataframe named trade_book to show the details of each trade.

We will initialise the variables to keep a track of the open position.

Here 0 represents no open position and 1 represents a long position.

Step 3 - Run the Backtest

Entry and exit logic for each data point

1. First, we will check if there is an open position or not. If there is no open position and the trading signal is buy (1), the entry price will be the same as the current price. We will record entry time and entry price and set current_position = 1. And based on the entry price, we will calculate the stop-loss and take-profit levels.
2. If there is an open position, then we will check whether the stop-loss or profit target is hit or not. If the price reaches the stop-loss or take-profit level, then we will close the open position and calculate the PnL. We will record the entry time, entry price, exit time, exit price and the PnL in a trade book. And at the end, we will set current_position = 0.

PnL calculation

To calculate the PnL, we will subtract the entry price from the exit price. Then, we will subtract the trading costs from the result. Transaction costs are incurred every time we enter a position and exit from the position. Therefore, the price is multiplied by 2 to get the slippage and transaction costs.

Now, let us calculate the PnL in Python.

Output:

Step 4 - Plot the Entry and Exits

Output:

Output:

With this Python code above, we learnt how to use fixed stop loss and take profit levels for exiting the market and avoiding the losses.

If you wish to learn more about using stop-loss and take profit with Python as a risk management method, you can refer to the course on Volatility trading strategies for Beginners.

In this course, you will also learn how to set dynamic triggers as stop loss and take profit levels which can adjust according to changing market conditions.

How to avoid common trading risks in the stock market?

Here are some essential tips for beginners to trade smart and protect their capital:

Educate Yourself

• Learn the basics: Before placing your first trade, grasp fundamental concepts like technical analysis and quantitative portfolio management strategies. You can also learn about practices like backtesting trading strategies which is essential for avoiding risks associated with discretionary trading. In addition to this, you can also learn about trading strategies for volatile markets with our course on volatility trading strategies.
• Develop a trading plan: Outline your strategy, entry and exit points, position sizing, and risk management rules. Stick to your plan for discipline and consistency.
• Stay informed: Keep up with market news, economic data, and company reports to make informed decisions.

Know Your Risk Tolerance Level

• Commence cautiously: Instead of risking your entire savings, initiate with a modest sum that won't significantly affect your financial well-being if lost.
• Implement stop-loss orders: These automatically offload your security when it hits a pre-established price, curbing potential losses.
• Clarify your risk tolerance: Be truthful about the amount of market fluctuations you can endure. Opt for strategies that match your comfort level.

Avoid Common Mistakes

• Chasing losses: Don't try to "catch up" after a bad trade. Stick to your plan and avoid emotional decisions.
• Overtrading: Don't trade too frequently. Excessive activity increases your risk and exposes you to unnecessary commissions.
• Ignoring risk management: Don't skip stop-loss orders or exceed your risk tolerance. Remember, protecting your capital is key.

Bonus Tip

Seek guidance from experienced traders or financial professionals. Their expertise can help you navigate the market with greater confidence and avoid costly mistakes.

Remember, risk management is not about eliminating risk, but about making informed decisions and protecting your capital for the long haul. By following these tips and continuously learning, you can build a solid foundation for your trading journey and navigate the market with greater confidence.

Suggested Read: Dynamic Money Management

Evolution of risk management in trading

Risk management in trading has undergone a significant transformation over the years, moving from simplistic rules to a more comprehensive and dynamic approach.⁽²⁾ ⁽³⁾ ⁽⁴⁾

Here's a summary of the key stages:

Early Days (Pre-1990s):

In the early days, traders focused on limiting exposure with basic rules and relied on intuition and experience. They used fundamental analysis and basic charting tools with limited historical data availability. Examples include position sizing and stop-loss orders based on gut feeling.

Transition Period (1990s-Early 2000s):

During the transition period, there was a shift towards quantitative analysis and increased risk awareness, driven by technological advancements and globalisation. Traders began adopting technology and basic risk management software, along with early backtesting and Value at Risk (VaR) calculations.

Modern Era (2000s-Present):

In the modern era, there's an emphasis on proactive risk identification and a holistic approach to risk management, driven by regulatory changes and financial crises. Innovations include AI-powered risk assessment, stress testing across diverse scenarios, and integrated risk management platforms, reflecting advanced technology adoption.

For a detailed insight into how risk management in trading has evolved over the years, you can check out this blog on Changing notions of risk management in trading.

The blog talks about everything from where risk management is required in the evolved trading domain to the regulatory risks and tips on how to ensure a good risk management practice.

Conclusion

With the trading practice, it is extremely important to make sure that your trades are secure with the right risk management in place. Having a good knowledge of risk management practices and strategies is a boon for any trader since it helps you minimise losses and maximise gains. With the right identification and evaluation of your risks, you can successfully manage them the same.

Risk management is also necessary to know about risks associated with day trading, intraday trading, and modern cryptocurrency trading. You can check out our course on Learning Track: Portfolio Management and Position Sizing using Quantitative Methods. With the courses in this learning track, you will learn to optimise the size of your trades, capital allocation in each of your strategies, and address the position sizing & portfolio management challenges using various quantitative techniques.

Also, you will explore factors like momentum, quality, value, and size to tap into consistent sources of alpha. Learn different position sizing techniques, dive into AI and Ml-based techniques such as hierarchical risk parity (HRP), and LSTM neural networks and apply the concepts learnt in live markets (paper trade).

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• Stop-loss and take profit for risk management in trading - Python notebook

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Author: Chainika Thakar

Note: The original post has been revamped on 4th March 2024 for recentness, and accuracy.

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