1
point
ryansmccoy
Joined 437 karma
“We all are learning, modifying, or destroying ideas all the time. Rapid destruction of your ideas when the time is right is one of the most valuable qualities you can acquire.
You must force yourself to consider arguments on the other side.”
— Charlie Munger
"Be skeptical, very skeptical, and even more skeptical. Great Analysts rarely accept anything at face value... Good Analysts always challenge what they've been told or given.Over time, if a source of information proves accurate, let it into your circle or trust...If we include the financial press, and everything distributed by companies, I'd say at least 75% of the information out there for consumption is misleading or omits an important piece of information relative to the topic."
- James Valentine, Former Morgan Stanley Analyst
"History doesn't repeat itself, but it often rhymes"
Mark Twain
https://linkedin.com/in/ryansmccoy
[ my public key: https://keybase.io/ryansmccoy; my proof: https://keybase.io/ryansmccoy/sigs/n3X093DdTAzB1J_9DGvlamPzYi0hBRwO7DolIBr4Mu0 ]
- Generating cash flow return on investments.
- If interested in Financial Markets, Quantlib is interesting.
https://github.com/lballabio/QuantLib
Also, there is a book which describes the different patterns used:
- As a former equity analyst, I would say that it's probably better just to ignore this analysis then read too much into it.
The performance of company's stock isn't based solely on whether the company reported a net income it's previous year; it's based on (imo) an ever fluctuating list of metrics, both controlled by the company (i.e. Return on Capital Invested, which net income is a component of) and not controlled by the company (i.e. cost for banks to borrow capital, future growth expectations, current market valuation).
- I'm pretty sure you can add multiprocessing parsing of csv with Python and Pandas. I think I've done it before.
- 2 points
- Washington University in St. Louis?
- 271 points
- I'm always reminded of the movie Grandma's Boy.
- 2 points
- Location: United States Remote: Sure
Willing to relocate: Yes
Technologies: Python, Go, int Java/Scala/C#, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases , Message Queues (RabbitMQ, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping, others
Résumé/CV: 13 years designing and building solutions in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
Developed cloud based web apps, data pipelines, market data systems, natural language text analytics, software/data architecture including micro-services, automation, network security/encryption, automated trading algorithms,and ETL systems;
Email: hn (at) ryansmccoy (dot) com
Linkedin: www.linkedin.com/in/ryansmccoy
Personal: www.ryansmccoy.com/
Portfolio: https://github.com/ryansmccoy
- SEEKING WORK | United States | Remote or Onsite
- 13 years designing and building mission-critical software, data, and cloud solutions for customers in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
- Developed cloud based web apps, custom dashboards, distributed data pipelines, market data systems, natural language text analytics, software/data architecture including micro-services, automated trading, and ETL systems;
Technologies: Python, Go, Java/Scala, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases (SQL, NoSQL, BigQuery, Redis, Cassandra, others), Message Queues (RabbitMQ/Celery, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping
(Email) hn (at) ryansmccoy (dot) com
(LinkedIn) www.linkedin.com/in/ryansmccoy
(Personal) www.ryansmccoy.com/
(Portfolio) https://github.com/ryansmccoy
- Again, I don't know, but I'm comfortable speculating that it is (partially). The other obvious reason that I could think of is that Colorado is a very appealing location to live (for some people) and when you are trying to attract talent, it could be used as an incentive to get them to join the company. Another reason, could be customers located in the area.
- I haven't seen the S-1, so I don't know.
- Unless I'm mistaken, according to Amazon's 10-K in 2002, they showed a Net loss of:
2002 - Net Loss: -$ 149 million
2001 - Net Loss: -$ 567 million
2000 - Net Loss: -$ 1.41 billion
1999 - Net Loss: -$ 719 million
1998 - Net Loss: -$ 124 million
Source: https://www.evernote.com/l/AUlIcX9k_elHUY7Fjsg749afPPxh3verZ...
- Yea, I don't know what's going on besides what I read.
I would much rather see the screenshot for myself then have someone else explain to me what they think the most important aspects of it are.
- Obviously, I don't know what's going on at Palantir besides what I've read, but I know a few things about Finance.
A tax loss isn't necessarily all bad news. If you have a tax loss in one year, you might be able to use that loss to offset profits in future years, to minimize taxes for your business in those years. This technique is called a tax loss carry forward because it takes a tax loss in one year and carries it into a future year.
So, what they might be doing is using this as an opportunity to "throw the baby out with the bathwater," as the saying goes. Show a bunch of losses prior to IPO (setting expectations low), then show gradually improving operating results. Investors love this kind of "story" because the potential for stock to go down is less then if they were to show a booming business right off the bat.
Finally, just to make a point about investors not necessarily being worried about a company showing losses, all you have to do is look at Amazon, who for years showed loses.
- Joe Rogan had some interesting guys, Graham Hancock & Randall Carlson, that talked about lost civilizations due to asteroid in 10,000 BC. Sounded like there is some controversy around it though, so take it however you'd like.
- Basically, you can think of the stock market (price) driven by two components: 1) an aggregate of all company profits in the stock market and 2) the cost of borrowing capital (discount rate).
1) Corporate Profits - From what I recall, corporate profits have been flat to down over recent quarters.
2) Discount Rate - The cost of borrowing capital has been falling as governments make access to capital easier for businesses. This has a huge effect on the valuation of the stock market compared to the impact of the profits. This is why the stock market continues to go up. The issue is that if you were to make access to capital harder, thus increasing the discount rate, companies in theory wouldn't be able to borrow as much, and therefore grow as much. Thus, the market would in theory go down, probably alot.
- SEEKING WORK | United States | Remote or Onsite
I've spent the last 13 years designing and building mission-critical software, data, and cloud solutions for customers in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
I've successfully developed cloud based web apps, custom dashboards, distributed financial data pipelines, low-latency and high-throughput market data systems, natural language text analytics, software/data architecture including micro-services, front, middle, back office automation, network security/encryption, automated trading algorithms, quantamental process automation, and ETL systems;
Technologies: Python, Go, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases (SQL, NoSQL, BigQuery, Redis, Cassandra, others), Message Queues (RabbitMQ/Celery, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping
(Email) hn (at) ryansmccoy (dot) com
(LinkedIn) www.linkedin.com/in/ryansmccoy
(Personal) www.ryansmccoy.com/
(Portfolio) https://github.com/ryansmccoy
- Location: United States
Remote: Sure
Willing to relocate: Yes
Technologies: Python, Go, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases , Message Queues (RabbitMQ, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping, others
Résumé/CV: I've spent the last 13 years designing and building mission-critical software, data, and cloud solutions for customers in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
I've successfully developed cloud based web apps, custom dashboards, distributed financial data pipelines, low-latency and high-throughput market data systems, natural language text analytics, software/data architecture including micro-services, front, middle, back office automation, network security/encryption, automated trading algorithms, quantamental process automation, and ETL systems;
Email: hn (at) ryansmccoy (dot) com
Linkedin: www.linkedin.com/in/ryansmccoy
Personal: www.ryansmccoy.com/
Portfolio: https://github.com/ryansmccoy
- 1 point
- Take a semester of Basic Accounting then a semester of Financial Accounting at a local Community College.
- It's a little complicated because all these metrics are like trying to compare apples, oranges, and bananas. So, it depends, and it's not necessarily the best metric to gauge the valuation of the markets.
It's price divided by earnings.
For price, I believe it's the sum of the market capitalization of all the companies in the S&P 500. If you divide by shares outstanding for all the companies, then you get price of the S&P 500 (price).
For earnings, is the sum of all the companies earnings (Net Income) in the S&P 500. If you divide earnings by shares outstanding, then you get earnings per share (earnings).
- SEEKING WORK | United States | Remote or Onsite
I've spent the last 13 years designing and building mission-critical software, data, and cloud solutions for customers in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
Basically, prefer solving challenging back-end problems, but can do some front-end (dashboards in react). Also, consider myself data engineer, but technically have data scientist skillset, so can do both.
I've successfully developed cloud based web apps, custom dashboards, distributed financial data pipelines, low-latency and high-throughput market data systems, natural language text analytics, software/data architecture including micro-services, front, middle, back office automation, network security/encryption, automated trading algorithms, quantamental process automation, and ETL systems;
Technologies: Python (celery, flask/django, numpy/ pandas/scipy, sqlalchemy, asyncio/multiprocessing/threading, others), Go, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases (SQL, NoSQL, BigQuery, Redis, Cassandra, others), Message Queues (RabbitMQ/Celery, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping
(Email) hn (at) ryansmccoy (dot) com
(LinkedIn) www.linkedin.com/in/ryansmccoy
(Personal) www.ryansmccoy.com/
(Portfolio) https://github.com/ryansmccoy
- SEEKING WORK | United States | Remote or Onsite
I've spent the last 13 years designing and building mission-critical software, data, and cloud solutions for customers in the Financial Markets, including Fortune 500 companies, Investment Managers, Hedge Funds, Venture Capital, Private Equity, and Data Vendors.
I've successfully developed cloud based web apps, custom dashboards, distributed financial data pipelines, low-latency and high-throughput market data systems, natural language text analytics, software/data architecture including micro-services, front, middle, back office automation, network security/encryption, automated trading algorithms, quantamental process automation, and ETL systems;
Technologies: Python (celery, flask/django, numpy/ pandas/scipy, sqlalchemy, asyncio/multiprocessing/threading, others), Go, Javascript (Node.js, Jquery, React), R, AWS, Azure, GCP, Hashicorp Terraform & Vault, Databases (SQL, NoSQL, BigQuery, Redis, Cassandra, others), Message Queues (RabbitMQ/Celery, ØMQ, Apache Kafka), Big Data (Apache Spark, Airflow/Kubeflow, Beam, Snowflake, Dask), Machine Learning/Natural Language Processing (Sklearn, Tensorflow, Pytorch, Gensim, NLTK, Spacy, ElasticSearch), Web Scraping
(Email) hn (at) ryansmccoy (dot) com
(LinkedIn) www.linkedin.com/in/ryansmccoy
(Personal) www.ryansmccoy.com/
(Portfolio) https://github.com/ryansmccoy
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