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コンテンツは Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal
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Leda Braga | Applying data science to investment strategies

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Manage episode 344169287 series 2706384
コンテンツは Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Leda Braga is the founder and CEO of Systematica Investments, a hedge fund that uses data science-driven models to support its investment strategies. Leda was born and raised in Brazil and found her way into the financial sector after getting her PhD in engineering and spending several years as an academic.

Her financial career started with seven years in investment banking at JP Morgan and then she joined the hedge fund startup BlueCrest in 2000. She explains that while her funds did very well during the 2008 financial crisis, the time felt like an existential crisis because you didn’t know if the major investment banks were going to survive. But she said it was a formative time and she learned many lessons. Several years after the financial crisis, she spun off her own firm, Systematica Investments focused on systematic trading.

Leda explains that systematic investment management is data science applied to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction.

Both discretionary traders and systematic traders are looking at information to try to make decisions. Those who do it on a discretionary basis tends to look at the data and make a decision to make money on a trade. Those that look at data on a systematic basis build data-driven processes for trading strategies for certain risk profiles and preferences that will produce consistent returns over time. She says the responsibility weighs heavily on her to ensure a good return because people's pensions are part of the money her firm manages.

While she believes strongly in the power of leveraging data science in investment, we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. She says that the only way it might be possible is if you’ve compartmentalized and narrowed the scope to the extent that you have a controlled amount of randomness. Learn more about Leda and systematic investing in her 2018 WIDS presentation, When Data Science is the Business.

RELATED LINKS
Connect with Leda on LinkedIn or Twitter
Find out more about Systematica Investments
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile

  continue reading

53 つのエピソード

Artwork
iconシェア
 
Manage episode 344169287 series 2706384
コンテンツは Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Stanford Women in Data Science (WiDS) initiative, Professor Margot Gerritsen, and Chisoo Lyons またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Leda Braga is the founder and CEO of Systematica Investments, a hedge fund that uses data science-driven models to support its investment strategies. Leda was born and raised in Brazil and found her way into the financial sector after getting her PhD in engineering and spending several years as an academic.

Her financial career started with seven years in investment banking at JP Morgan and then she joined the hedge fund startup BlueCrest in 2000. She explains that while her funds did very well during the 2008 financial crisis, the time felt like an existential crisis because you didn’t know if the major investment banks were going to survive. But she said it was a formative time and she learned many lessons. Several years after the financial crisis, she spun off her own firm, Systematica Investments focused on systematic trading.

Leda explains that systematic investment management is data science applied to investment. The systematic approach makes the investment process less reliant on the random nature of forecasting and more reliant on risk control in portfolio construction.

Both discretionary traders and systematic traders are looking at information to try to make decisions. Those who do it on a discretionary basis tends to look at the data and make a decision to make money on a trade. Those that look at data on a systematic basis build data-driven processes for trading strategies for certain risk profiles and preferences that will produce consistent returns over time. She says the responsibility weighs heavily on her to ensure a good return because people's pensions are part of the money her firm manages.

While she believes strongly in the power of leveraging data science in investment, we’re not yet at a point where AI allows us to do “autonomous investing” because there's a large element of randomness in markets and relatively sparse data so learning algorithms have limited use. She says that the only way it might be possible is if you’ve compartmentalized and narrowed the scope to the extent that you have a controlled amount of randomness. Learn more about Leda and systematic investing in her 2018 WIDS presentation, When Data Science is the Business.

RELATED LINKS
Connect with Leda on LinkedIn or Twitter
Find out more about Systematica Investments
Connect with Margot Gerritsen on Twitter (@margootjeg) and LinkedIn
Find out more about Margot on her Stanford Profile

  continue reading

53 つのエピソード

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