Ethics and Professionalism

What is a profession? The CFA handbook describes it as. 

  • based on specialized knowledge and skills. 
  • based on service to others. 
  • practiced by members who share and agree to adhere to a common code of ethics. 

A distinction is also made between a customer and a client. A customer has one or several transactions, paying per transaction. A client on the other hand, hires a professional for their skill and expertise. It is a relationship based on trust. 

The CFA institute communicates its Code of Conduct. This gives members clarity and a common set of standards. It also increases trust and value for CFA members in the eyes of clients or prospective clients. The CFA Institute Code and Standards are based on the shared principles of honesty, integrity, transparency, diligence, and placing client interests first. Rule-based standards are often narrowly defined, applying to specific groups of individuals in specific circumstances. Principle-based standards, such as those of CFA Institute, apply to all candidates and members at all times regardless of title, position, occupation, geographic location, or specific situation. 

The CFA codes and standards help cement a spirit of responsibility and trust in the members. 

Challenges to Ethical Conduct 

Why is it so important to study ethics or adhere to a prescribed code of conduct in a professional group? The reason is simple but not always understood. The reason is to be able to navigate tricky professional situations with ethical behavior. The first step is to accept that such unchartered territory exists and falling back on an ethics standard is more foolproof than simply relying on one’s goodness. 

Members of the investment industry feel that their firms and they are very ethical. However retail investors and the general public trust the financial services industry the least. This is known as the overconfidence bias. In this bias, people tend to rely on their internal moral compass, though it has been proved that many factors lead to unethical behavior. 

The second challenge to ethical behavior in professional situations is known as situational influence. This is the impact of the peer group or surrounding environmental and cultural factors that influence decision-making. Sometimes a group behaving ethically will influence a person to behave likewise. However, the opposite is also true. People underestimate the importance they place on their organisations or work groups while making crucial decisions. 

Other factors that influence decision-making are bonuses, promotions and hierarchy. It is not unusual to find honest, well-intentioned professionals behaving in an unethical manner because they value these rewards and positions of prestige. This leads to individuals focusing on the immediate and short-term gains, without much thought to long-term consequences. 

Another reason professionals often go wrong in certain situations is over-compliance. They go too much by the book rather than addressing what the situation requires. When employees are simply complying, rather than questioning if they are behaving ethically in a situation, things can go very wrong. Again, this is a result of a very short-term view. 

In the case of Energy major Enron, the CEO and top staff complied with all accounting norms. Unfortunately, the methods they used were unethical and unfair. They used complex accounting to inflate revenues and hide the true extent of debt. They went ahead with blinders, forgetting that they had a responsibility to shareholders. They were held guilty for criminal conspiracy and were responsible for erosion in shareholder value, large loss of jobs and the beginning of people’s lack of faith in large organisations that seemed to be using the rulebook and not practicing it in spirit.

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Data Science in Finance: 9-Book Bundle

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Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
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  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

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